Liquid AI jobs

Liquid Labs - Research Engineer Boston, Cambridge, Massachusetts, United States About Liquid Labs Research has been core to Liquid AI from the beginning. Liquid Labs gives that work a formal home; an internal research accelerator driving fundamental breakthroughs in the science of building intelligent, personalized, and adaptive machines. Our origins trace back to MIT CSAIL, where the foundational work on Liquid Neural Networks defined a new class of dynamical, efficient sequence-processing architectures. That research became the basis for Liquid Foundation Models (LFMs). Scalable, multimodal models built for real-world deployment in resource-constrained environments. At Liquid Labs, we extend that lineage - pushing forward the frontier of efficient, adaptive intelligence through both fundamental research and practical engineering. We work hand-in-hand with Liquid's core foundation model and systems teams to translate theory into deployed capability - defining a new generation of intelligent systems that are both powerful and efficient. About The Role: As a Research Engineer, you'll join a small, high-context team exploring the limits of adaptive intelligence. You'll design and implement novel architectures, training methods, and inference strategies to redefine what efficient AI can do. You'll operate at the intersection of research and engineering - translating scientific ideas into working systems, publishing where it drives the field forward, and deploying where it changes what's possible. While San Francisco and Boston are preferred, we are open to other locations in the United States. This Role Is For You If: - Work fluently in Python and frameworks such as PyTorch, JAX, or TensorFlow -

Product Marketing Manager San Francisco, United States, New York About Liquid AI Spun out of MIT CSAIL, we build general-purpose AI systems that run efficiently across deployment targets, from data center accelerators to on-device hardware, ensuring low latency, minimal memory usage, privacy, and reliability. We partner with enterprises across consumer electronics, automotive, life sciences, and financial services. We are scaling rapidly and need exceptional people to help us get there. The Opportunity You will be our first product marketing hire, reporting to the VP of Marketing at the intersection of product, communications, and go-to-market. Your job is to ensure that what we build lands with the right audiences and is supported by the infrastructure to do it repeatedly. This is a high-ownership role for a strategic marketer who can deeply understand Liquid's capabilities and the needs of our enterprise buyers and technical users - and is also motivated by Liquid's story. You must thrive in ambiguity and are energized by standing up a function from scratch. No playbook required. What We're Looking For We need someone who is a: - Builder: You use modern tools and AI, including Claude Code, for content creation, campaign work, and rapid prototyping; you're comfortable spinning up a demo or proof-of-concept when it serves a launch or sales moment, and you can teach your team to do the same - Translator: You move between deeply technical source material and clear, credible market messaging. You know when an answer from engineering needs more clarity before it

Account Executive San Francisco, United States, Boston, Remote About Liquid AI Spun out of MIT CSAIL, we build general-purpose AI systems that run efficiently across deployment targets, from data center accelerators to on-device hardware, ensuring low latency, minimal memory usage, privacy, and reliability. We partner with enterprises across consumer electronics, automotive, life sciences, and financial services. We are scaling rapidly and need exceptional people to help us get there. The Opportunity We have early enterprise traction and a product that solves real problems for technical buyers. What we don't have yet is a repeatable commercial engine. This is one of our first sales hires, and you will own the full sales cycle: prospecting through close, selling Liquid Foundation Models to technical leaders at enterprises across consumer electronics, automotive, life sciences, and financial services. You'll work directly with our founders and GTM leadership to shape pricing, packaging, and deal strategy while building the playbook the team scales on. What We're Looking For We need someone who: - Is obsessed with selling: Not management, not BD, not player-coach. We need someone energized by running deals, with meaningful revenue closed, and the drive to keep doing it, where every deal matters. - Knows the AI landscape: You've recently sold AI/ML infrastructure, developer tools, or platform products. You understand how technical buyers evaluate model performance, latency, and deployment tradeoffs. This context is non-negotiable at our stage. - Has deep empathy: We value sellers who build trust through a genuine understanding of prospects, their business,

Solutions Architect San Francisco, United States, Boston About Liquid AI Spun out of MIT CSAIL, we build general-purpose AI systems that run efficiently across deployment targets, from data center accelerators to on-device hardware, ensuring low latency, minimal memory usage, privacy, and reliability. We partner with enterprises across consumer electronics, automotive, life sciences, and financial services. We are scaling rapidly and need exceptional people to help us get there. The Opportunity Liquid AI is building a solutions architecture function from scratch. You will be one of the first SAs, working directly with the Head of Solutions Architecture and across the go-to-market org to own customer engagements end-to-end. Our models are purpose-built for environments where memory, latency, and power are binding constraints - edge devices, mobile, embedded systems, and on-prem infrastructure where frontier models simply cannot run. You will work at this boundary every day. Customers range from AI-native companies to enterprise organizations exploring AI for the first time. Your job is to bridge the gap between what our models can do and what customers believe is possible, then deliver on that promise from technical validation through go-live. What We're Looking For We need someone who: - Technical builder : You can download a model, build a demo, and present it to a customer. You are as comfortable in a Jupyter notebook as you are in a boardroom. - Creative problem solver : You see opportunities where customers see limitations. You can take a small, efficient model and show an enterprise why

Member of Recruiting Staff - Technical Recruiter San Francisco, United States About Liquid AI Spun out of MIT CSAIL, we build general-purpose AI systems that run efficiently across deployment targets, from data center accelerators to on-device hardware, ensuring low latency, minimal memory usage, privacy, and reliability. We partner with enterprises across consumer electronics, automotive, life sciences, and financial services. We are scaling rapidly and need exceptional people to help us get there. The Opportunity Liquid AI is hiring fast across the business, and our high-output recruiting team is how we win the talent that defines us. This is a full-cycle role in San Francisco, owning hiring across the full spectrum: engineering and research, product, solutions architecture, GTM, and G&A. Recruiting here is a highly ambiguous, build-from-scratch environment. We are hiring for most of these roles for the first time, so you will define brand-new roles, build the rubrics, coach stakeholders, and bring in exceptional people. You will run your searches with high agency and minimal supervision, with a manager who will still mentor you. What We're Looking For We need someone who: - Operates as a talent partner, not just a recruiter: You measure yourself by what is best for the business, not seats filled. - Builds in ambiguity: You are energized by undefined problems and create structure where there is none. - Thinks strategically and proactively: You read the data, anticipate where a search will stall, and unblock it before it becomes a problem, rather than reacting after the

Member of Technical Staff - Post Training, Applied (Audio) San Francisco, United States, Boston, Remote About Liquid AI Spun out of MIT CSAIL, we build general-purpose AI systems that run efficiently across deployment targets, from data center accelerators to on-device hardware, ensuring low latency, minimal memory usage, privacy, and reliability. We partner with enterprises across consumer electronics, automotive, life sciences, and financial services. We are scaling rapidly and need exceptional people to help us get there. The Opportunity LFM2.5-Audio is Liquid's end-to-end multimodal speech and text language model. At 1.5B parameters, it handles speech-to-speech conversation, ASR, and TTS without requiring separate components, making it uniquely suited for real-time, on-device deployment. We're now bringing this model to enterprise customers. The core challenge: teaching audio models to understand user intents and translate them into structured tool calls. Think voice-driven function calling, where a spoken request triggers the right API, extracts the right parameters, and confirms back to the user in natural speech. This role sits at the intersection of frontier audio models and real-world deployment. You'll own the applied post-training work that adapts LFM2.5-Audio for customer use cases end-to-end, from data generation through delivery. Unlike most roles that force a trade-off between customer impact and foundational work, this one gives you both: deep ownership over how audio models are adapted, evaluated, and shipped, and a direct line into the evolution of Liquid's post-training and audio stacks. If you care about data quality, evaluation, and making models actually work in production, this is

Member of Technical Staff - Post Training, Applied (Vision) San Francisco, United States, Boston, Remote About Liquid AI Spun out of MIT CSAIL, we build general-purpose AI systems that run efficiently across deployment targets, from data center accelerators to on-device hardware, ensuring low latency, minimal memory usage, privacy, and reliability. We partner with enterprises across consumer electronics, automotive, life sciences, and financial services. We are scaling rapidly and need exceptional people to help us get there. The Opportunity This is a rare chance to sit at the intersection of frontier vision-language models and real-world deployment. You'll own applied post-training work for VLMs end-to-end for some of the world's largest enterprises, while still contributing directly to Liquid's core multimodal model development. Unlike most roles that force a trade-off between customer impact and foundational work, this role gives you both: deep ownership over how vision-language models are adapted, evaluated, and shipped, and a direct line into the evolution of Liquid's multimodal post-training stack. If you care about visual understanding, data quality, evaluation, and making VLMs actually work in production, this is a chance to shape how applied multimodal AI is done at a foundation model company. What We're Looking For We need someone who: - Takes ownership: Owns VLM post-training projects end-to-end, from customer requirements through delivery and evaluation. - Thinks end-to-end: Can reason across visual data curation, training, alignment, and evaluation as a single system. - Is pragmatic: Optimizes for model quality and customer outcomes over publications or theory. - Communicates

Member of Technical Staff - ML Engineer / Scientist (JP Localization) Tokyo, Japan Work With Us At Liquid, we're not just building AI models-we're redefining the architecture of intelligence itself. Spun out of MIT, our mission is to build efficient AI systems at every scale. Our Liquid Foundation Models (LFMs) operate where others can't: on-device, at the edge, under real-time constraints. We're not iterating on old ideas-we're architecting what comes next. We believe great talent powers great technology. The Liquid team is a community of world-class engineers, researchers, and builders creating the next generation of AI. Whether you're helping shape model architectures, scaling our dev platforms, or enabling enterprise deployments-your work will directly shape the frontier of intelligent systems. This Role Is For You If: - You like building LLM pipelines and agents for diverse use cases, and enjoy catching and fixing edge cases where LLMs may fail - You're a native Japanese speaker and want to further improve LLM capabilities in Japanese - You're motivated by the challenge of adapting foundation models to new languages, cultures, and enterprise workflows Desired Experience: - Deep understanding of the Japanese model evaluation landscape and familiarity with Japanese pre-training data sources - Experience using modeling and inference tools such as Huggingface inference, vLLM, and cloud APIs What You'll Actually Do: - Identify, collect, and curate diverse high-quality Japanese text, audio, and multimodal datasets - Design methods to synthetically generate or augment Japanese training data when needed - Ensure datasets meet enterprise-grade quality, coverage,

Member of Technical Staff - Distributed Training Engineer San Francisco, United States, Boston, Remote About Liquid AI Spun out of MIT CSAIL, we build general-purpose AI systems that run efficiently across deployment targets, from data center accelerators to on-device hardware, ensuring low latency, minimal memory usage, privacy, and reliability. We partner with enterprises across consumer electronics, automotive, life sciences, and financial services. We are scaling rapidly and need exceptional people to help us get there. The Opportunity Our Training Infrastructure team is building the distributed systems that power our next-generation Liquid Foundation Models. As we scale, we need to design, implement, and optimize the infrastructure that enables large-scale training. This is a high-ownership training systems role focused on runtime/performance/reliability (not a general platform/SRE role). You'll work on a small team with fast feedback loops, building critical systems from the ground up rather than inheriting mature infrastructure. While San Francisco and Boston are preferred, we are open to other locations. What We're Looking For We need someone who: - Loves distributed systems complexity: Our team builds systems that keeps long training runs stable, debugs training failures across GPU clusters, and improves performance. - Wants to build: We need builders who find satisfaction in robust, fast, reliable infrastructure. - Thrives in ambiguity: Our systems support model architectures that are still evolving. We make decisions with incomplete information and iterate quickly. - Aligns with team priorities and delivers: Our best engineers align with team priorities while pushing back with data when they see

Sr. Product Manager, AI Liquid Cooling & Rack-Scale Solutions US - HQ (US001) | Country United States | State California | City San Jose Job Req ID: 26994 About Supermicro: Supermicro® is a Top Tier provider of advanced server, storage, and networking solutions for Data Center, Cloud Computing, Enterprise IT, Hadoop/ Big Data, Hyperscale, HPC and IoT/Embedded customers worldwide. We are the #5 fastest growing company among the Silicon Valley Top 50 technology firms. Our unprecedented global expansion has provided us with the opportunity to offer a large number of new positions to the technology community. We seek talented, passionate, and committed engineers, technologists, and business leaders to join us. Job Summary: Join the global leader in AI/HPC Infrastructure. As a Senior Product Manager at Supermicro, you will own the end-to-end roadmap for our high-density, liquid-cooled rack solutions. You will drive "First-to-Market" innovation, transforming cutting-edge NVIDIA, Intel, and AMD silicon into scalable, Direct Liquid Cooling (DLC) systems that power the world's largest AI clusters. Essential Duties and Responsibilities: Includes the following essential duties and responsibilities (other duties may also be assigned): Architect the Rack-Scale Roadmap (L10/L11): Define specifications for 100kW+ power delivery and Direct Liquid Cooling (DLC) manifolds, ensuring Supermicro's integrated racks support the next generation of 1200W+ GPUs. Accelerate "First-to-Market" Launches: Partner with silicon giants to align our thermal and mechanical designs with their release cycles, ensuring we ship production-ready clus

Member of Technical Staff - Post Training, Applied San Francisco, United States, Boston, Remote About Liquid AI Spun out of MIT CSAIL, we build general-purpose AI systems that run efficiently across deployment targets, from data center accelerators to on-device hardware, ensuring low latency, minimal memory usage, privacy, and reliability. We partner with enterprises across consumer electronics, automotive, life sciences, and financial services. We are scaling rapidly and need exceptional people to help us get there. The Opportunity This is a rare chance to own applied post-training work end-to-end for text workloads, adapting Liquid Foundation Models for some of the world's largest enterprise customers. You will act as the technical bridge between customer requirements and model delivery. You will lead engagements from scoping through evaluation, with full ownership over how text models are adapted and shipped. Between engagements, you will build reusable applied workflows and tooling that accelerate future delivery. If you care about data quality, evaluation design, and making language models actually work in production for real customers, this is the role. What We're Looking For We need someone who: - Takes ownership: Owns customer post-training projects end-to-end, from requirements through delivery and evaluation. - Thinks end-to-end: Can reason across data generation, instruction tuning, alignment, and evaluation as a single system. - Is pragmatic: Optimizes for model quality and customer outcomes over publications or theory. - Communicates clearly: Can translate between customer needs and internal technical teams, and push back when needed. The Work - Act as the technical owner

Member of Technical Staff - Multi-Modal, Vision San Francisco, United States About Liquid AI Spun out of MIT CSAIL, we build general-purpose AI systems that run efficiently across deployment targets, from data center accelerators to on-device hardware, ensuring low latency, minimal memory usage, privacy, and reliability. We partner with enterprises across consumer electronics, automotive, life sciences, and financial services. We are scaling rapidly and need exceptional people to help us get there. The Opportunity The VLM team builds vision-language models that run on-device, under tight latency and memory constraints, without sacrificing quality. We have released four best-in-class models and we're just getting started. This team owns the full VLM pipeline end-to-end: from researching new architectures and training algorithms through data curation, evaluation, and deployment. You'll join a focused, hands-on group that works directly on models and collaborates closely with our pretraining, post-training, and infrastructure teams. Success here is measured by the capability of the models we ship. Minimal qualifications: - Hands-on experience in training or evaluating VLMs with demonstrated experimental rigor. - Ability to turn research ideas into scalable implementations, refine and iterate through hypotheses. - Proficiency in Python and at least one deep learning framework. - M.S. or Ph.D. in Computer Science, Mathematics, or a related field; or equivalent industry experience. This role is for you if you have experience in some of the following: - Building or optimizing multimodal training or data pipelines. - Experience with distributed training (DeepSpeed, FSDP, Megatron-LM, etc.). - Multimodal post-training experience (SFT, preference

Member of Technical Staff - Edge Inference Engineer San Francisco, United States, Boston, Remote About Liquid AI Spun out of MIT CSAIL, we build general-purpose AI systems that run efficiently across deployment targets, from data center accelerators to on-device hardware, ensuring low latency, minimal memory usage, privacy, and reliability. We partner with enterprises across consumer electronics, automotive, life sciences, and financial services. We are scaling rapidly and need exceptional people to help us get there. The Opportunity Our Edge Inference team compiles Liquid Foundation Models into optimized machine code that runs on resource-constrained devices: phones, laptops, Raspberry Pis, and watches. We are core contributors to llama.cpp and build the infrastructure that makes efficient on-device AI possible. You will work directly with the technical lead on problems that require deep understanding of both ML architectures and hardware constraints. This is high-ownership work where your code ships to production and directly impacts model performance on real devices. While San Francisco and Boston are preferred, we are open to other locations. What We're Looking For We need someone who: - Works autonomously: Given a target device and performance goal, you figure out how to get there without hand-holding. You diagnose bottlenecks, prototype solutions, and iterate until you hit the target. - Thinks at the hardware level: You understand cache hierarchies, memory access patterns, and instruction-level optimization. You can reason about why code is slow before reaching for a profiler. - Bridges ML and systems: You understand how neural networks work mathematically (matrix operations,

Member of Technical Staff - Multi-Modal - Audio San Francisco, United States, Boston About Liquid AI Spun out of MIT CSAIL, we build general-purpose AI systems that run efficiently across deployment targets, from data center accelerators to on-device hardware, ensuring low latency, minimal memory usage, privacy, and reliability. We partner with enterprises across consumer electronics, automotive, life sciences, and financial services. We are scaling rapidly and need exceptional people to help us get there. The Opportunity Our Audio team is building frontier speech-language models that handle STT, TTS, and speech-to-speech in a single architecture. This role sits at the center of applied audio model development, working directly with the technical lead to ship production systems that run on-device under real-time constraints. You will own critical workstreams across data pipelines, evaluation systems, and customer deployments. If you want high ownership on rare technical problems in a small, elite team where your code ships, this is the role. What We're Looking For We need someone who: - Builds first, theorizes later: You ship working systems, not just notebooks. Production-grade code is your default, not a stretch goal. - Owns outcomes end-to-end: From data pipelines to customer deployments, you take responsibility for the full stack without waiting for someone else to handle the hard parts. - Thrives under constraints: On-device, low-latency, memory-limited systems excite you. You see constraints as design parameters, not blockers. - Ramps quickly on new territory: Gaps in specific subdomains are fine if you close them fast. You seek out

Member of Technical Staff - Applied ML, RecSys Boston, Cambridge, Massachusetts, United States About Liquid AI Spun out of MIT CSAIL, we build general-purpose AI systems that run efficiently across deployment targets, from data center accelerators to on-device hardware, ensuring low latency, minimal memory usage, privacy, and reliability. We partner with enterprises across consumer electronics, automotive, life sciences, and financial services. We are scaling rapidly and need exceptional people to help us get there. The Opportunity This is a rare chance to apply frontier sequential recommendation architectures to real enterprise problems at scale. You will own applied ML work end-to-end for recommendation system workloads, adapting Liquid Foundation Models for customers who need personalization and ranking capabilities that run efficiently under production constraints. Unlike most recommendation roles that are siloed into a single product surface, this role gives you full ownership over how large-scale recommendation models are adapted, evaluated, and deployed for enterprise customers. Between engagements, you will build reusable applied tooling and workflows that accelerate future delivery. If you care about data quality at scale, user behavior modeling, and making recommendation systems actually work in enterprise production environments, this is the role. What We're Looking For We need someone who: - Takes ownership: Owns customer recommendation system engagements end-to-end, from requirements through delivery and evaluation. - Thinks at scale: Can reason about user interaction data, sequential modeling, feature engineering, and evaluation across large-scale production systems. - Is pragmatic: Optimizes for measurable customer outcomes (engagement, conversion, revenue lift) over theoretical novelty.

Member of Technical Staff - ML Research Engineer, Data San Francisco, United States, Boston, Remote About Liquid AI Spun out of MIT CSAIL, we build general-purpose AI systems that run efficiently across deployment targets, from data center accelerators to on-device hardware, ensuring low latency, minimal memory usage, privacy, and reliability. We partner with enterprises across consumer electronics, automotive, life sciences, and financial services. We are scaling rapidly and need exceptional people to help us get there. The Opportunity Our Data team powers Liquid Foundation Models across pre-training, vision, audio, and emerging modalities. Public data sources are plateauing. Model performance increasingly depends on purpose-built datasets. We need ML-minded engineers who can collect, filter, and synthesize high-quality data at scale. We treat data as a research problem, not an infrastructure problem. Our engineers run experiments, design ablations, and measure how data decisions move model quality. We will match you to the team where you can grow the fastest and have the most impact: pre-training, post-training RL, vision-language, audio, or multimodal. While San Francisco and Boston are preferred, we are open to other locations. What We're Looking For We need someone who: - Thinks like a researcher, ships like an engineer: We need people who form hypotheses, run experiments, and measure results. Our engineers understand deep-theoretical research, and our researchers ship production systems. - Learns fast and adapts: We work across modalities that evolve weekly. We need people who pick up new domains quickly and thrive with ambiguity. - Obsesses over data quality:

Senior AI Scientist France Join Pigment: The AI Platform Redefining Business Planning Pigment is the AI-powered business planning and performance management platform built for agility and scale. We connect people, data, and processes in one intuitive, feature-rich solution, empowering every team-from Finance to HR-to build, adapt, and align strategic plans in real time. Founded in 2019, Pigment is one of the fastest-growing SaaS companies globally. Industry leaders like Unilever, Snowflake, Siemens, and DPD use Pigment daily to make more informed decisions and confidently navigate any scenario. With a team of 600+ across Paris, London, New York, Toronto, San Francisco and Austin, we've raised nearly $400M from top-tier investors and were named a Visionary in the 2024 Gartner® Magic Quadrant™ for Financial Planning Software. At Pigment, we take smart risks, celebrate bold ideas, and challenge the status quo-all while working as one team. If you're driven by innovation and ready to make an impact at scale, we'd love to hear from you. Pigment is seeking an experienced AI Scientist with a strong CS background and experience with GenAI and Agentic Architectures to join our innovative and fast-paced team. In this role, you will work on AI projects and play a crucial part in developing and refining our product. If you are passionate about machine learning, have a background generative AI and natural language processing, and thrive in a collaborative and high-impact environment, we'd love to hear from you! What you will do : - Work on AI features built on top

AI Research Engineer, FAIR Chemistry Menlo Park, CA; San Francisco, CA Meta is seeking a researcher to join the Fundamental AI Research (FAIR) Chemistry team, a research organization focused on advancing the state-of-the-art in AI. In this role, you'll work with world-class researchers at FAIR on fundamental and exploratory research. Our current projects include new datasets, models, and capabilities to accelerate chemistry and materials science. Our organization is motivated by producing new science to understand intelligence and technology towards achieving advanced machine intelligence. In this specific role, you will develop and enhance capabilities for modeling complex battery and electrolyte chemistries. Responsibilities: - Provide technical leadership for a scientific direction in AI for science. - Perform research that advances the state-of-the-art in AI for chemistry and materials science. - Work closely with internal and external partners to realize impact of methods advances. - Work towards long-term goals, while identifying intermediate milestones. - Influence progress of relevant research communities by producing publications. - Open source high quality code and produce reproducible research. - Enhance the capabilities of FAIR chemistry models in complex chemical environments like electrolytes or strong applied fields. Minimum qualifications: - First-authored or last-authored publications at peer-reviewed conferences, such as ICML, NeurIPS, ICLR, and other similar venues, or domain scientific journals - Master's Degree or PhD in AI, computer science, data science, science/engineering, or related technical fields - Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience - Research background in machine learning, artificial intelligence,

AI Talent Pool Paris, Paris, France Are you passionate about artificial intelligence and want to contribute to revolutionizing the healthcare sector? Doctolib develops cutting-edge AI solutions to improve access to care and facilitate the daily lives of healthcare professionals and patients. Our AI/ML teams work on large-scale projects: medical natural language processing, recommendation systems, clinical process automation, and much more. We leverage AI ethically across our products to empower patients and health professionals. Discover our AI vision here ! By joining our AI talent pool, you will be contacted as a priority for our future opportunities in the following areas: The areas of expertise we are looking for : - ML Engineering & MLops : Python proficiency, Model deployment, Evaluation frameworks (Offline/Online) & Model monitoring - Natural Language Process (NLP) : Clinical NLP, LLM expertise Deep Learning or Speech / Language modeling - LLMops & Generative AI : Prompt Engineering, RAG, LLM fine-tuning, Embedding models, Agent frameworks - Automatic Speech Recognition (ASR) : Voice Activity Detection, Interactive voice assistants, audio processing, Speech-to-text - Search, Ranking, information retrieval - Reasoning capabilities : Logical reasoning, Multi-step reasoning, Chain-of-thought approaches - Knowledge representation : Knowledge graphs, ontologies, Semantic modeling - Reinforcement Learning : RL algorithms, Policy optimization, Reward modeling Most of our positions require: Before reading further, if you don't have the exact profile described below, but you think this job description matches your skills and aspirations, we still encourage you to apply. - Hold a degree in computer science, applied mathematics, data

HPC & AI - Power & Cooling Product Manager Spring, Texas, United States of America Embrace the opportunity to become a HPC & AI Product Manager and lead the strategy, development, and management of advanced liquid cooling and power distribution solutions for high-performance computing environments. Drive innovation, collaborate with cross-functional teams, and shape the future of HPC & AI technology at Hewlett Packard Enterprise.

Senior Manager, AI Lead - Treasury & Cash Management Agents New York, New York; Chicago, Illinois Aon is in the business of better decisions At Aon, we shape decisions for the better to protect and enrich the lives of people around the world. As an organization, we are united through trust as one inclusive team and we are passionate about helping our colleagues and clients succeed. What the day will look like Design and deploy AI agents that: Monitor cash positions, liquidity forecasts, and bank data feeds Automate variance explanations and exception escalation Support scenario modeling for liquidity and funding decisions Build agentic workflows that interact with Kyriba, bank portals, Workday, and the Finance Data Platform Ensure all cash‑related agents adhere to: Data lineage, completeness, and reconciliation standards Segregation of duties and access controls Partner with Treasury, Accounting, and Risk to embed controls directly into agent execution Define reusable agent patterns for: Bank reconciliation Payment diagnostics Liquidity planning support Contribute to enterprise standards for agent security, monitoring, and lifecycle management How this opportunity is different This is your unique opportunity to own the development of treasury‑focused agents and intelligent bots that automate free cash flow forecasting, liquidity forecasting, bank connectivity monitoring, cash positioning, and exception handling-while maintaining strong data governance, controls, and auditability. Skills and experience that will lead to success 8+ years of experience in Treasury, Cash Management, or Finance Technology Strong understanding of global cash operations, banking connectivity, and liquidity processes Experience with treasury platforms (e.g., Kyriba or

DCP-Liquid Cooling System Architect (Global)-Shenzhen China Responsible for the overall design of the liquid cooling platform, including the structural design of CDUs, cold plates, manifolds, and racks;​ Conduct simulation analysis and optimization of liquid cooling systems to enhance system efficiency and stability;​ Oversee testing processes for liquid cooling products to ensure compliance with technical specifications and industry standards;​ Collaborate with the team on the integration and commissioning of liquid cooling systems across various application scenarios;

Principal Engineer, Engineering AI Productivity Remote, United States | Remote We're not just building better tech. We're rewriting how data moves and what the world can do with it. With Confluent, data doesn't sit still. Our platform puts information in motion, streaming in near real-time so companies can react faster, build smarter, and deliver experiences as dynamic as the world around them. It takes a certain kind of person to join this team. Those who ask hard questions, give honest feedback, and show up for each other. No egos, no solo acts. Just smart, curious humans pushing toward something bigger, together. One Confluent. One Team. One Data Streaming Platform. ABOUT THE ROLE: We're seeking a strategic and hands-on technical leader to define and drive Confluent's internal agentic AI capabilities, and adoption of smart, automated decisioning systems for R&D productivity. In this role, you will partner across Engineering, Security, and IT to scale AI usage across our platform to accelerate value for customers and the business. You will operationalize a vision where AI augments developers, automates routine tasks, and orchestrates intelligent workflows that unlock new usage patterns. WHAT YOU WILL DO: - Strategy & Vision - Define and evangelize the AI and agentic flows strategy - aligning business metrics, engineering needs, and platform opportunities. - Build a roadmap for introducing agentic automation features, smart assistants, and AI-driven workflows across all aspects of engineering. - Identify and resolve bottlenecks across the entire product and software development life cycle. - Cross-Functional Leadership -

Senior ML Science Manager Dublin, Ireland Fin is the AI Customer Agent company on a mission to help businesses provide perfect customer experiences. Our AI Agent Fin is the highest-performing AI Customer Agent on the market today, enabling businesses to deliver impeccable, always-on customer support across the customer journey - from service, to sales, to ecommerce. Powered by our own AI models, Fin resolves complex customer issues end-to-end across every channel, with minimal set-up and integration. Fin can also be combined with our natively integrated Intercom help desk for one single system that is designed to meet the needs of modern day support teams. Founded in 2011, Fin became one of the fastest growing companies and remains one of the largest private software companies in the world with nearly 30,000 global businesses using our products to transform their customer support. Driven by our core values, we push boundaries, build with speed and intensity, and relentlessly deliver incredible value to our customers. What's the opportunity? Fin's AI Group is responsible for defining new ML products, researching appropriate algorithms and technologies, and rapidly getting first prototypes in our customers' hands. We are extremely product-focussed. Our team of 50+ ML scientists, ML engineers, designers and researchers works in partnership with other teams across the whole company. We move to production fast, often shipping to beta in weeks after a successful offline test. We are very passionate about applying machine learning technology, and have productized everything from classic supervised models, to cutting-edge unsupervised clustering