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Liquid AI

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  • Member of Recruiting Staff - Technical Recruiter

    San Francisco, United States

    unspecified Salary not disclosed

    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

  • Account Executive

    San Francisco, United States, Boston, Remote

    remote Salary not disclosed

    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,

  • Product Marketing Manager

    San Francisco, United States, New York

    unspecified Salary not disclosed

    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

  • Solutions Architect

    San Francisco, United States, Boston

    unspecified Salary not disclosed

    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 Technical Staff - Post Training, Applied (Vision)

    San Francisco, United States, Boston, Remote

    remote Salary not disclosed

    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 - Applied ML, RecSys

    Boston, Cambridge, Massachusetts, United States

    unspecified Salary not disclosed

    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 - Post Training, Applied (Audio)

    San Francisco, United States, Boston, Remote

    remote Salary not disclosed

    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 - Edge Inference Engineer

    San Francisco, United States, Boston, Remote

    remote Salary not disclosed

    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 - Post Training, Applied

    San Francisco, United States, Boston, Remote

    remote Salary not disclosed

    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 - Audio

    San Francisco, United States, Boston

    unspecified Salary not disclosed

    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

  • Liquid Labs - Research Engineer

    Boston, Cambridge, Massachusetts, United States

    unspecified Salary not disclosed

    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 -

  • Member of Technical Staff - Multi-Modal, Vision

    San Francisco, United States

    unspecified Salary not disclosed

    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 - ML Engineer / Scientist (JP Localization)

    Tokyo, Japan

    unspecified Salary not disclosed

    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 - ML Research Engineer, Data

    San Francisco, United States, Boston, Remote

    remote Salary not disclosed

    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:

  • Member of Technical Staff - Distributed Training Engineer

    San Francisco, United States, Boston, Remote

    remote Salary not disclosed

    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

  • Member of Technical Staff - GPU Performance Engineer

    San Francisco, United States, Boston, Remote

    remote Salary not disclosed

    Member of Technical Staff - GPU Performance 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 models and workflows require performance work that generic frameworks don't solve. You'll design and ship custom CUDA kernels, profile at the hardware level, and integrate research ideas into production code that delivers measurable speedups in real pipelines (training, post-training, and inference). Our team is small, fast-moving, and high-ownership. We're looking for someone who finds joy in memory hierarchies, tensor cores, and profiler output. While San Francisco and Boston are preferred, we are open to other locations. What We're Looking For We need someone who: - Works profiler-first: You use tools like Nsight Systems / Nsight Compute to find bottlenecks, validate hypotheses, and iterate until improvements show up in end-to-end benchmarks. - Bridges theory and practice: You can translate ideas from papers into implementations that are robust, testable, and performant. - Executes independently: Given an ambiguous bottleneck, you can drive from profiling to kernel/integration changes to benchmarked results to maintained ownership. - Cares about the details: Memory hierarchy, occupancy, launch configs, tensor core utilization, bandwidth vs compute limits. The Work - Write high-performance