Lila Sciences jobs

Co-op, Machine Learning for Digital Twins Cambridge, MA USA Your Impact at LILA Lila Sciences builds AI systems that accelerate discovery across the physical and life sciences. Within Physical Sciences AI, our team partners with the diverse experimental groups to build digital twins of experimental campaigns, focusing on calibrated, uncertainty-aware models that enable higher-throughput, higher-quality use of Lila's AI Science Facilities (AISF). As an ML for Digital Twins Co-Op, you will work on building, training, and evaluating ML models for physical and experimental systems. You will get hands-on experience with operator learning, surrogate modeling, and uncertainty quantification, shipping work that directly informs how next-generation AISF experiments are designed and run. What You'll Be Building - Contribute to ML models for scientific and experimental systems, focused on a well-defined digital twin sub-problem - Build and train surrogate, operator-learning, or physics-informed models against experimental and simulation data, with mentor guidance - Calibrate models, quantify uncertainty, and validate against data flowing from active AISF experimental campaigns - Frame open-ended scientific questions as concrete ML tasks with clear datasets, baselines, and evaluation criteria - Document findings and share results in cross-departmental collaboration through write-ups and presentations What You'll Need to Succeed - Pursuing a Master's or PhD in Machine Learning, Computer Science, Applied Mathematics, Physics, Materials Science, Chemical Engineering, Mechanical Engineering, Electrical Engineering, or a related quantitative field (PhD preferred) - Strong programming skills in Python and hands-on experience with ML frameworks such as PyTorch, JAX, TensorFlow, or similar - Experience applying machine learning

Research Scientist, Dexterous Manipulation & Robot Learning Cambridge, MA USA Your Impact at LILA As a Robotics Scientist at Lila, you will lead the research and development of autonomous robotic systems that serve as the intelligent physical infrastructure of our scientific superintelligence platform. You'll develop novel algorithms and deploy intelligent robotic solutions that interact seamlessly with human scientists and complex lab environments. Your work will accelerate our mission by enabling fully autonomous workflows for scientific discovery, combining cutting-edge robotics, machine learning, and systems engineering. What You'll Be Building - Pioneering approaches for precise and dexterous robotic manipulation that leverage foundation models, reinforcement learning, diffusion-based methods, and human guidance to enable adaptive and intelligent robotic systems capable of complex tasks across diverse scientific environments - Developing novel human-robot interaction frameworks that incorporate imitation learning, and learning from human guidance, feedback, demonstrations and corrections, creating intelligent robotic agents that can seamlessly integrate with human scientific workflows and rapidly adapt to new experimental contexts - Advancing dexterous manipulation research through cutting-edge machine learning approaches, including diffusion models and adaptive learning algorithms, that synthesize multi-modal sensing (tactile, visual, and language) to develop generative skill representation sand sophisticated motor learning policies for intelligent robotic systems - Designing autonomous robotic systems with trust calibration mechanisms, enabling intelligent agents that can dynamically adjust their behaviors based on contextual information in complex scientific tasks What You'll Need to Succeed - Ph.D. in Robotics, Machine Learning, Computer Science, or a related field with demonstrated expertise in foundation models for

Principal / Sr. Principal BioML Scientist San Francisco, CA USA Your Impact at LILA Lila is building a platform where AI and automation co-evolve to solve the hardest problems in medicine. Within Life Sciences AI (LSAI), we are standing up a new AI for Cell Biology team to develop autonomous-science capabilities for cellular and tissue biology, spanning single-cell omics, perturbation biology, spatial profiling, imaging, genetics, and multi-modal experimental data. We are seeking a Principal or Sr. Principal BioML Scientist to be a co-architect of how Lila's autonomous-science platform changes cell biology and to own the applied and translational BioML charter that turns those platform capabilities into real-world therapeutic impact. The team's ML lead owns core model strategy and inference architecture; the Engineering lead owns platform infrastructure; this role adds the applied scientific perspective into platform shape: deciding what closed loops are worth running, what kinds of scientific questions become tractable when AI and lab automation co-evolve, and what evidence standard turns a model output into an experimental decision. The platform isn't something this role consumes ; it's something this role helps build , from the applied science side. This role grows and leads the team's applied science footprint : a group of domain-embedded scientists working across disease areas and therapeutic modalities (cell therapy, nucleic-acid delivery, small molecule). The initial applied focus is target identification as the entry point into cell-biology-grounded therapeutic discovery, with the scope broadening over time as the team and the platform mature. This is a senior individual

Senior / Principal Technical Program Manager, Life Sciences AI Cambridge, MA USA; San Francisco, CA USA Your Impact at LILA Lila is building a platform where AI and automation co-evolve to solve the hardest problems in medicine. Within Life Science AI (LSAI), Lila develops large-scale generative models and reasoning frameworks - spanning biological sequences, molecular structures, and multimodal experimental data - that power automated scientific discovery and feed Lila's closed-loop "Lab-in-the-Loop" discovery engine. As that work scales, the surface area of cross-functional coordination across model research, evaluation, experimental science, and downstream integration is growing fast. We are looking for a Senior or Principal Technical Program Manager to join our AI Research team and bring structure, accountability, and delivery cadence to Life Sciences AI. You will be the connective tissue between model researchers, computational and wet-lab scientists, automation and software teams, and the broader Lila organization. The successful candidate thrives in ambiguity, communicates exceptionally well across audiences, and knows how to build clarity and momentum on a fast-paced, rapidly scaling team. In addition, you must be genuinely curious about how foundation models for biology drive scientific discovery and able to clearly articulate what each program is trying to deliver. What You'll Be Building - Establish and operate the delivery cadence and rhythm for the Life Sciences AI organization - driving program planning, status, milestone tracking, and risk management across foundation model research, evaluation, and integration with Lila's closed-loop discovery engine. - Serve as the key communication interface between LSAI and partner

Staff ML Engineer, Life Sciences AI San Francisco, CA USA Your Impact at LILA Lila is building a platform where AI and automation co-evolve to solve the hardest problems in medicine. Within Life Science AI (LSAI), software engineers build the systems that connect generative models, scientific data, and experimental workflows into reliable, production-grade pipelines powering Lila's protein design and engineering campaigns. We're hiring a Staff ML Engineer, Life Sciences AI to lead software infrastructure development for our protein design and engineering pipelines. This is a senior IC role focused on the engineering systems that surround and support our ML stack - pipeline orchestration, data flow between computational and experimental systems, integration of new tools and methods, and the developer experience that lets LSAI move fast on commercial partnership deliverables. What You'll Be Building - Architect and build software infrastructure powering Lila's protein design and engineering pipelines: orchestration, data flow, APIs, and integration with experimental systems. - Own the engineering side of LSAI's "Lab-in-the-Loop" lifecycle - connecting computational outputs to experimental inputs and feeding results back into design workflows. - Onboard new tools and methods developed by AI scientists and ML engineers into production-ready systems used in commercial partnership campaigns. - Partner cross-functionally with ML researchers, scientists, and platform engineers to translate research code into reliable, scalable systems. - Set engineering standards for LSAI software - design reviews, CI/CD, testing, observability, reproducibility - and mentor senior engineers as the team grows. - Diagnose and resolve reliability, performance, and scaling bottlenecks in

Senior / Engineer II, AI Lab Research Engineer Cambridge, MA USA; San Francisco, CA USA Your Impact at LILA Lila Sciences is the world's first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science. We are pioneering a new age of boundless discovery by building the capabilities to apply AI to every aspect of the scientific method. We are introducing scientific superintelligence to solve humankind's greatest challenges, enabling scientists to bring forth solutions in human health, climate, and sustainability at a pace and scale never experienced before. Learn more about this mission at www.lila.ai At Lila, we are uniquely cross-functional and collaborative. We are actively reimagining the way teams work together and communicate. Therefore, we seek individuals with an inclusive mindset and a diversity of thought. Our teams thrive in unstructured and creative environments. All voices are heard because we know that experience comes in many forms, skills are transferable, and passion goes a long way. If this sounds like an environment you'd love to work in, even if you only have some of the experience listed below, please apply. What You'll Be Building - Agentic AI for science: Systems that perform sequential decision‑making and multi‑step reasoning to solve domain‑specific problems. - Workflow/code generation: From natural language intent to typed, executable steps for lab instruments. - Evaluation & reliability: Benchmarks, test suites, and telemetry to measure capability and quantify progress toward scientific goals. What You'll Need to Succeed - PhD or Masters in a quantitative discipline (e.g.,

ML Research Scientist I/II, Multimodal Data Extraction Cambridge, MA USA Your Impact at LILA As a ML Research Scientist - Multimodal Data Extraction , you will advance Lila's vision of scientific superintelligence by developing foundation models that autonomously read, interpret, and structure scientific knowledge across text, images, and experimental data in the physical sciences. Your research will help unify the world's scientific information into machine-understandable form, powering reasoning, prediction, and autonomous discovery across materials science and chemistry. What You'll Be Building - Research and develop AI systems that extract and structure knowledge from diverse scientific sources. - Design and fine-tune large language, multi-modal and specialized models for factual, interpretable data extraction. - Build scalable pipelines for unstructured and heterogeneous scientific data , integrating text, tables, and visuals. - Collaborate with domain experts to align extracted data with real-world discovery workflows. - Publish research that advances the state of the art in multimodal understanding and AI-driven knowledge extraction. What You'll Need to Succeed - PhD (or equivalent research experience) in Computer Science, Chemistry, Materials Science, or related field. - Expertise in machine learning , NLP , and vision-language modeling using PyTorch and Hugging Face Transformers . - Proven ability to train, fine-tune, and evaluate LLMs and multimodal models for scientific data extraction. - Strong understanding of data structures and representations used in the physical sciences. - Demonstrated research impact through publications, preprints, or open-source work (e.g., NeurIPS, ICLR, ICML, ACL, EMNLP, Scientific Journals). Bonus Points For - Experience with multimodal fusion

Staff Engineer, Data Platform Cambridge, MA USA; San Francisco, CA USA Your Impact at LILA Lila Sciences is building the software platform that makes automated scientific discovery possible. At the heart of that platform is data: raw outputs from laboratory instruments, experimental model results, curated public datasets, and the scientific literature that contextualizes all of it. The data platform team is responsible for the infrastructure that moves, stores, transforms, and surfaces this data across the organization. We are looking for a Staff Engineer to set the technical direction for our core data infrastructure: ingestion frameworks, storage architecture, orchestration patterns, and the interfaces that let scientists and ML researchers work with data reliably at scale. You will work closely with software engineers, machine learning researchers, and lab scientists to understand requirements and translate them into durable platform capabilities. This is a role for engineers who care deeply about how data systems are designed. You will establish the architectural patterns and engineering standards the broader team builds on, mentor engineers across the data platform group, and make technical decisions that compound over time. What You'll Be Building - Data Platform Architecture: Design and evolve the core data infrastructure that ingests, stores, and serves data across scientific and ML workflows. Make principled build-vs-buy decisions and establish architectural patterns adopted by the broader engineering organization. - Ingestion and Integration: Build reliable pipelines that bring in data from diverse sources: laboratory instruments, public scientific datasets, and external research literature. Own the interfaces between upstream producers

Staff / Principal Research Engineer, AI Safety, Technical Mitigations Cambridge, MA USA; London, UK; San Francisco, CA USA Your Impact at LILA We're building a talent-dense, high-agency AI safety team at Lila that will engage all core teams within the organization (science, model training, lab integration, etc.), to prepare for risks from scientific superintelligence. The initial focus of this team will be to build and implement a bespoke safety strategy for Lila, tailored to its specific goals and deployment strategies. This will involve technical safety strategy development, broader ecosystem engagement, safety-focused evaluations, safety systems to mitigate risks, and a safety research agenda that explores longer-term needs such as oversight of superintelligent scientific systems. We're seeking a Technical Mitigations Lead, to lead the build out of safety systems at Lila for the safe deployment of our scientific capabilities to the world. Given the novelty of Lila's workflows, integrating frontier-class language models with narrow scientific tools and lab-based automation, this role will require the design and deployment of technical safeguards beyond the current state-of-the-art. We expect the person in this role to start off the initial mitigations build-out, and then slowly build a team to support this function. What You'll Be Building - Set the build and research strategy for Lila's safety systems, across scientific data analysis and generation pipelines, safety post-training, refusal classifiers, automated safety-testing / red-teaming systems, and monitoring systems. - Conduct initial safeguards experimentation and buildout for Lila's specific scientific needs, and subsequently lead a small team to execute

Scientist I/II, mRNA Translation Dynamics Cambridge, MA USA Your Impact at LILA We are seeking a curious, driven, and collaborative Scientist to join our discovery platform team focused on decoding mRNA translation dynamics. You will develop workflows that utilize next-generation pooled screening strategies like polysome profiling and Ribo-seq to to generate rich biological datasets that feed directly into Lila's machine learning models. This is a unique opportunity to help invent a new approach to biological discovery by integrating synthetic biology, high-throughput experimentation, and intelligent automation. What You'll Be Building - Design and execute high-throughput pooled screening campaigns to interrogate mRNA translation dynamics across diverse sequence and structural contexts - Develop and optimize cell-free and in-cell assay systems for quantitative measurement of translation efficiency, kinetics, and regulation across different cell environments - Collaborate closely with computational and ML teams to define data requirements, validate model predictions, and close the loop between experiment and prediction - Establish and refine next-generation library design strategies, leveraging combinatorial and rational approaches to explore sequence space efficiently - Analyze and interpret complex biological datasets, distilling key findings into actionable insights for platform advancement - Contribute to the development of automated and semi-automated experimental pipelines to increase throughput and consistency What You'll Need to Succeed - MSc with 4+ years of industry or academic experience, or PhD in a relevant field (molecular biology, bioengineering, synthetic biology, chemical biology, etc.) - Deep expertise in mRNA biology and translation regulation - Experience with next-generation sequencing based assays of mRNA

Scientist, Epitaxial Thin Film Synthesis Cambridge, MA USA Your Impact at LILA As an epitaxial thin film scientist on the Materials Science team at Lila Sciences, you will serve as the lead for synthesis of high-quality epitaxial thin films, contributing to the discovery of novel quantum materials with emergent electronic and magnetic properties. Your domain expertise at the intersection of materials growth, structural characterization, and electronic transport will translate into the method design and automated workflow to enable high throughput experimentation that Lila's autonomous science platform learns from. This work contributes to Lila's materials science programs focused on the predictive design of novel material families and compounds with novel functional properties. You will design growth and characterization protocols that shape how the platform interrogates new material systems, and your experimental outputs feed directly into the next round of campaigns. You will partner with experimentalists, systems engineers, and machine learning scientists, providing the functional-properties knowledge that keeps closed-loop campaigns scientifically grounded and accelerating toward next-generation materials systems. What You'll Be Building - Grow single-crystal epitaxial thin films and heterostructures using epitaxial deposition methods (sputtering, pulsed-laser deposition, molecular beam epitaxy). - Optimize deposition conditions (substrate temperature, flux ratios, growth rate) to achieve atomically sharp interfaces and target crystal phases. - Analyze XRD and AFM data. - Design, fabricate, and measure electronic transport devices, including temperature-dependent resistivity, Hall effect, and magnetoresistance. - Analyze magneto-transport data to extract physical parameters and identify emergent ground states. - Maintain deposition and characterization equipment; troubleshoot hardware and

Research Scientist, Frontier Capabilities Cambridge, MA USA; San Francisco, CA USA Your impact at LILA We're building a talent-dense, high-agency research team to develop the next generation of learning systems and reasoning algorithms for agentic LLMs. Our work sits at the intersection of large language models, post-training, and scientific reasoning, with the goal of enabling systems that learn from experience, reason effectively, and improve through interaction . Scientific domains present a distinct set of challenges that make this problem uniquely hard. Feedback is sparse and delayed - experiments take days or weeks, not milliseconds. Ground truth is expensive or contested. Distribution shift is structural, as instruments, techniques, and knowledge bases evolve continuously. The hypothesis space is vast and reward signal is thin. Existing benchmark do not capture these nuances. The goal is to build systems that can operate effectively in this scientific regime. This role spans a few complementary directions. Candidates are expected to bring deep expertise in one (ore more) of the following areas. In the event of cross-track expertise, please select the one you align to the most. Our interview process will be catered to verifying the chosen expertise area. Expertise Area 1 - Agentic system building Focus: Build systems that autonomously propose, execute, and verify scientific hypotheses over long time horizons. - Create and analyze long-running auto-research systems that propose and verify hypotheses - Design planning frameworks for agentic systems operating over long, sparse feedback loops - Design memory architectures that allow agents to build and retrieve

Associate / Engineer I, ResOps, Synthesis Cambridge, MA USA Your Impact at LILA We are seeking a motivated and detail-oriented Associate Engineer to join our high-throughput Research Operations team. In this role, you will focus on hands on materials synthesis of various powders and porous medias as well as characterization of materials using spectroscopy and microscopy techniques. You will gain hands-on experience with state-of-the-art automation technologies while contributing directly to projects that drive our company's growth. This is an excellent opportunity for candidates excited to learn engineering principles, work with advanced laboratory automation, and develop a strong foundation in scientific operations in a dynamic, shift-based environment. What You'll Be Building - Perform materials synthesis for materials including zeolites, MOFS, coolants, quantum dots, and lipid nanoparticles - Prepare reagents, solutions/electrolytes , samples, and consumables to ensure seamless continuation of experiments across shifts - Follow detailed standard operating procedures (SOPs) to ensure accuracy, reproducibility, and throughput - Monitor workflow performance, troubleshoot common issues, and escalate when necessary to ensure continuity between shifts - Record, organize, and maintain experimental data with high attention to detail - Identify opportunities to improve efficiency and streamline processes across shift operations - Flag unusual features or results for scientist review - Collaborate with scientists and engineers to support diverse projects across life sciences and physical sciences What You'll Need to Succeed - Bachelor's degree in Chemistry, Physics, Chemical/Materials Engineering, or a related field (or equivalent laboratory experience) - Attention to detail and strong organizational skills - 1

Co-op, LLMs for Decision Making Cambridge, MA USA Your Impact at LILA Lila Sciences builds AI systems that accelerate discovery across the physical and life sciences. Within Physical Sciences AI, our decision making efforts develop the algorithms that drive experimental decision-making, closing the loop between models, experiments, and the next thing to try. We're now exploring how large language models can extend that capability: encoding domain priors, proposing candidates, reasoning over campaign history, and pairing naturally with established algorithms like Bayesian optimization for sample-efficient search. As an LLMs for Decision Making Co-Op, you will work at the intersection of LLMs and Bayesian optimization, prototyping and evaluating approaches that combine language model reasoning with principled experimental design. Your work will land in the decision making stack that powers experimental campaigns across Lila's AI Science Facilities. What You'll Be Building - Contribute to LLM-based decision-making methods for experimental campaigns, focused on a well-defined sub-problem - Prototype approaches that combine LLM reasoning with Bayesian optimization, active learning, or design of experiments, with mentor guidance - Build evaluation frameworks that benchmark LLM-augmented strategies against established Bayesian baselines - Help integrate promising methods into the decision making stack used across physical sciences campaigns - Document findings and share results through write-ups, presentations, or contributions to internal libraries What You'll Need to Succeed - Pursuing a Master's or PhD in Machine Learning, Computer Science, Statistics, Applied Mathematics, Physics, Chemistry, Materials Science, or a related quantitative field - Strong programming skills in Python and familiarity with ML

Senior Software Engineer, Scientific System of Record Cambridge, MA USA; San Francisco, CA USA Your Impact at LILA Join us in shaping the future of science! We are seeking Senior Software Engineers with full stack experience to join our Scientific System of Record Team (SSR), where you'll collaborate with software engineers, lab scientists, and machine learning engineers to build cutting-edge tools for automated scientific analysis and more. If you thrive in a collaborative, fast-paced environment and bring best practices in git, development workflows, and user-centered design, we want to hear from you! About The Team The Scientific System of Record Team (SSR) builds the memory layer for Lila's operations. It answers two questions: what did we plan to build? and what actually happened? These systems connect scientific intent to physical reality. Together with the data and automation teams, their systems ensure reproducibility and close the Design-Build-Test-Learn (DBTL) loop. What You'll Be Building - Lab Execution and Scientific Workflows: Build systems that model scientific intent, experiment planning, protocol execution, sample and asset state, operational events, and results capture across complex lab workflows. - User Interfaces and APIs: Design and implement high-quality, secure, and well-documented UIs and APIs that support scientists, automation systems, ML workflows, and AI-driven applications. - Application Development: Build front-end and backend services with a focus on performance, maintainability, and reliability. - Data and System Modeling: Develop domain models, schemas, indexes, and data contracts across SQL, NoSQL, vector databases, data lakehouses, and other scientific data systems. - Reliability, Performance,

Scientist II, Mechanics & Extreme Materials Cambridge, MA USA Your Impact at LILA As a Scientist II, Mechanics & Extreme Materials on the Materials Science team at Lila Sciences, you will be the technical expert driving closed-loop learning for extreme materials. Your domain depth in coatings and metal alloys, durability testing, and microstructural analysis will directly shape the experimental methods and automated workflows that power Lila's autonomous science platform. This role sits at the center of Lila's extreme materials program focused on the predictive design of hard, wear-resistant, and corrosion-resistant coatings for aerospace, defense, and industrial applications. You will define how the platform interrogates the performance of new alloy and coating systems, what data it generates, and how those outputs feed back into the next experimental cycle. You will work alongside experimentalists, systems engineers, and machine learning scientists, providing the mechanical and metallurgical judgement that keeps closed-loop campaigns scientifically rigorous and accelerating toward next-generation coating and extreme material solutions. What You'll Be Building - Work with Program Lead to drive the strategy for closed-loop campaigns on anti-wear and anti-corrosion materials. - Develop, execute and optimize characterization and testing workflows for extreme environment materials, including coatings and bulk materials. Focus areas include: microstructural, mechanical, and tribological analysis for process-structure-property relationships. - Collaborate with experimentalists, systems engineers, and ML scientists to integrate characterization outputs into autonomous closed-loop workflows and ML training datasets. - Lead experimental design and analysis to extract key materials properties and performance parameters for ML training. - Troubleshoot characterization

Senior Software Engineer, Lab Software Cambridge, MA USA Your Impact at LILA Join us in shaping the future of science! We are seeking Senior Software Engineers with backend experience to join our Lab Software Team (LaS), where you'll collaborate with software engineers, lab scientists, and machine learning engineers to build cutting-edge tools for automated scientific analysis and more. If you thrive in a collaborative, fast-paced environment and bring best practices in git, development workflows, and user-centered design, we want to hear from you! About The Team The Lab as Software Team (LaS) acts as the physical and virtual execution layer for Lila's AI-driven science. This connects the Lila App to AI Science Factories (AISFs), the mechanism through which experiments are dispatched to real instruments. This team owns the full stack of lab integration: orchestration of labflows, instrument integrations, bi-directional data transfer, and the UI/UX that AI scientists and operators use to interact with the lab. What You'll Be Building - Design & Build UI and APIs: Design and build high-performance, secure, and well-documented UI and APIs that integrate with AI-driven applications. - Database Architecture & Scaling: Develop schemas and manage diverse data systems (SQL, NoSQL, Vector DBs, and others) for optimal performance and scalability. - Application Development: Drive the implementation of front-end and backend services, focusing on performance, maintainability, and reliability. - Performance & Reliability: Diagnose and optimize system bottlenecks, ensuring high availability and low-latency performance across large-scale workloads. - Cloud & Infrastructure: Leverage AWS services, Kubernetes and modern DevOps

Life Sciences Technical Program Manager Cambridge, MA USA Your Impact at LILA Lila Sciences is applying its autonomous science platform to some of the most complex challenges in biology and medicine - from protein therapeutics and nucleic acid design to antibody discovery and cell therapies. The Life Sciences experiment team is running programs where the pace of autonomous experimentation demands real operational rigor, and we're looking for a Technical Program Manager to provide it. You'll work closely with the Life Sciences domain lead who sets scientific strategy and direction, while you own the execution layer: the project plans, the cross-functional dependencies, and the blockers that need to be surfaced and resolved before they become problems. You won't be directing scientific work - you'll be creating the conditions for it to happen at speed and at scale. This is a role for someone who genuinely loves the craft of program management in complex, technical environments. What You'll Be Building - Own day-to-day program execution within the Life Sciences experiment team - maintaining project plans, tracking milestones, and ensuring agentic science programs are delivered on time and with rigor - Serve as the primary operational interface between the Life Sciences team and platform partners (Automation, ML, Engineering) communicating needs, aligning on priorities, and keeping dependencies visible and resolved - Translate scientific program goals into structured workstreams that enable autonomous experimentation to run efficiently and at scale - Run program rituals including sprint planning, stand-ups, and retrospectives with a bias toward purposeful meetings,

Staff / Principal Industrial Engineer Cambridge, MA USA Your Impact at LILA Lila Sciences is seeking an Industrial Engineer to own and architect the physical layout of our AI Science Factories. In this role, you will be responsible for integrating lab instrumentation and robotics into a cohesive, efficient factory design. You will collaborate closely with hardware engineering, science teams, robotics, facilities, and operations to create layouts that are safe, scalable, and engineered for high-throughput autonomous research. This role is central to translating complex cross-functional requirements into a factory environment that enables Lila's next-generation scientific capabilities. What You'll Be Building - Own the end-to-end factory layout, including spatial planning, adjacency mapping, and utility requirements - Integrate lab instrumentation layouts from diverse scientific and engineering domains into a unified, high-performance factory design. - Collaborate with hardware, robotics, science, automation, safety, and facilities teams to incorporate requirements for utilities, safety, accessibility, maintenance, and environmental controls. - Build and maintain accurate 2D and 3D CAD models of the full factory layout; drive version control and change management for layout updates. - Work with robotic teams to run simulations and scenario analyses to assess throughput, operational efficiency, and bottlenecks. - Partner with construction, facilities, and external vendors to ensure physical buildouts reflect the intended design and can scale with future demand. - Develop standards, documentation, and layout guidelines for new lab instrumentation and factory expansions. What You'll Need to Succeed - BS/MS degree in Industrial Engineering, Mechanical Engineering, Manufacturing Engineering, Systems Engineering, or related field.

Staff Forward Deployed Engineer, Physical Sciences (Level Flexible) Cambridge, MA USA Your Impact at LILA We're seeking extraordinarily talented Forward Deployed Engineers across Engineer, Senior Engineer, and Lead Engineer levels to join our growing team. Our FDEs are high-performing generalists with a broad set of skills, in addition to depth in one or more focus areas. This FDE, Physical Sciences role requires experience in chemistry and materials science in an industrial manufacturing setting. As an FDE, you'll embed directly with our internal biotech and materials science teams as well as external customers, living their problems and rapidly building solutions that transform their research capabilities. You'll leverage our AI and software platform tools while developing bespoke features that address each customer's unique challenges. This isn't traditional software development-it's high-velocity, AI-fueled, high-impact engineering where you'll ship production code in days, not months. What You'll Be Building - Developing, wrapping, and adapting domain-specific tools for data analysis, process optimization, molecular modeling, and more - Deploying custom, agentic scientific workflows that integrate with industrial instruments (PLCs, control software) and data systems (ERP, MES, SCADA) - Building real-time data pipelines that transform messy scientific data into actionable insights - Creating intuitive interfaces that make complex AI/ML capabilities accessible to scientists - Architecting solutions that scale from proof-of-concept to enterprise-wide deployment What You'll Need to Succeed - Domain expertise in chemistry, materials science, or related field - Outstanding communication skills-you'll interface directly with both executives and the manufacturing line - Experience with data engineering, ETL pipelines,

Senior Software Engineer, Data Cambridge, MA USA; San Francisco, CA USA Your Impact at LILA Join us in shaping the future of science! We are seeking Senior Software Engineers with backend experience to join our Data Platform Team (Data), where you'll collaborate with software engineers, lab scientists, and machine learning engineers to build cutting-edge tools for automated scientific analysis and more. If you thrive in a collaborative, fast-paced environment and bring best practices in git, development workflows, and user-centered design, we want to hear from you! About The Team The Data Platform Team (Data) builds and support the data systems that underpins Lila's AI Science Factory™. Every experiment run in our labs, every measurement from an instrument, and every signal from our operational systems flows through the platform they build. Their work spans real-time ingestion, large-scale analytical storage, workflow orchestration, and the self-service tools scientists, engineers, and ML teams use to go from raw measurements to discoveries. They build the data backbone of Scientific Superintelligence™, so the science moves faster and each experiment makes the next one smarter. What You'll Be Building - Design & Build APIs: Design and build high-performance, secure, and well-documented APIs that integrate with AI-driven applications. - Database Architecture & Scaling: Develop schemas and manage diverse data systems (SQL, NoSQL, Vector DBs, and others) for optimal performance and scalability. - Performance & Reliability: Diagnose and optimize system bottlenecks, ensuring high availability and low-latency performance across large-scale workloads. - Cloud & Infrastructure: Leverage AWS services, Kubernetes and

Analyst / Senior Analyst, Corporate Strategy Cambridge, MA USA Your Impact at LILA The Analyst, Corporate Strategy sits at the center of how Lila Sciences thinks about its future. This is a high-visibility, high-impact individual contributor role within the Strategy and Corporate Development function. The team is small, senior, and fast-moving. You will support the full range of the function's work: corporate strategy development, competitive intelligence, M&A and corporate development, enterprise and strategic planning, and OKR-linked incentive tracking. You will work directly with senior leaders across the company and have regular exposure to board and investor-level materials. This role is well-suited to someone early in their career who is intellectually curious, operates with strong analytical rigor, and is energized by working at the intersection of science, technology, and business strategy. What You'll Be Building - Synthesize market, competitive, and internal data to support strategic decision-making - Build and maintain structured analyses, frameworks, and presentations for senior leadership and board audiences - Track emerging trends in AI, life sciences, and physical sciences relevant to Lila's strategic positioning - Support deal processes including landscape mapping, initial screening, and diligence preparation - Maintain competitive intelligence across key market segments, including company tracking, partnership activity, and technology developments - Build and refresh comp sets and market benchmarks in support of M&A and partnership evaluation - Support the annual strategic planning cycle, including data gathering, cross-functional synthesis, and documentation - Assist in OKR infrastructure: tracking, reporting, and maintaining the linkage between role-level objectives and company-level

Research Scientist I/II, Statistical Mechanics and Dynamics Cambridge, MA USA Your Impact at LILA Your role in our Physical Sciences division will focus on designing and implementing state-of-the-art simulation approaches to model transport, kinetics, rare events, and reaction networks, and integrating them with AI-driven platforms for materials discovery. Your work will be integral to our efforts in predicting, designing, and controlling the behavior of complex materials and molecular systems, and their acceleration via agentic AI. You will partner with diverse teams at Lila, including machine learning experts working on scientific superintelligence and materials science experts performing real-world experiments. What You'll Be Building - Develop and extend molecular dynamics and Monte Carlo algorithms to capture rare events, non-equilibrium processes, transport phenomena, and mapping complex reaction networks. - Build scalable simulation workflows that integrate statistical mechanics methods with machine learned interatomic potentials and agentic AI frameworks. - Design methods for coupling dynamics simulations with experimental observables to enable closed-loop verification and discovery with automated labs. - Collaborate with computational scientists, machine learning experts, and platform engineers to advance the fidelity and scalability of simulation-driven materials discovery. - Establish reproducible, modular software pipelines for statistical mechanics and dynamics simulations that can be deployed on HPC and cloud-based infrastructure. What You'll Need to Succeed - PhD or equivalent research/industry experience in Physics, Chemistry, Chemical Engineering, Mechanical Engineering, Applied Mathematics, or related fields. - Strong background in statistical mechanics, free energy calculations, reaction mapping, non-equilibrium dynamics, and rare-event sampling. - Demonstrated expertise with molecular dynamics,

Commercial Counsel (Associate Director/Director) Cambridge, MA USA; San Francisco, CA USA Your Impact at LILA As Commercial Counsel (Director/Associate Director) at Lila Sciences, you will be a foundational member of the legal team, responsible for shaping and scaling legal capabilities in a fast-moving, frontier technology environment. This is a dynamic, high-ownership role focused on structuring and negotiating a wide array of commercial agreements, while also advising on legal and regulatory considerations surrounding artificial intelligence, scientific research, and cutting-edge technology development. You'll partner cross-functionally across science, engineering, business development, and operations to enable Lila's mission to accelerate discovery with AI. We're looking for an experienced, entrepreneurial attorney with 6-10 years of legal experience-someone who is excited to work at the intersection of law, AI, and science. You should thrive in environments that demand both precision and creativity, be comfortable navigating ambiguity, and bring a strong sense of judgment and empathy to collaborative problem-solving. If you're passionate about building scalable legal infrastructure, engaging deeply with novel technologies, and evolving the practice of law using AI, this is the role for you. What You'll Be Building - Draft, negotiate, and manage a broad range of commercial agreements (e.g., SaaS, IP licensing, vendor, partnership, and R&D collaboration contracts) - Provide pragmatic legal counsel to internal stakeholders across the business, product, and science functions, balancing legal risk with strategic objectives - Serve as a thought partner on regulatory and ethical considerations related to the use of AI in scientific and commercial contexts - Help build