Liquid AI, Inc
Member of Technical Staff - ML Engineer (Product)
Liquid AI, Inc, San Francisco, California, United States, 94199
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.
While San Francisco and Boston are preferred, we are open to other locations in United States.
This Role Is For You If: You're an ML engineer who loves building for developers, not just models You get excited by turning research breakthroughs into usable platforms You want to make powerful model customization accessible to non-ML engineers You think in systems and abstractions-how to package complexity into simple APIs and flows You enjoy collaborating across research, infra, and product to bring technology into builders' hands Desired Experience: Strong applied ML experience (training, fine-tuning, evaluation, deployment) Experience building tools, SDKs, or infrastructure for developers Deep understanding of how to expose ML capabilities through clean abstractions Familiarity with model optimization, distillation, or on-device inference a plus Comfort translating research into practical, productized workflows What You'll Actually Do: Build and evolve Liquid's internal ML-to-developer platform layer Translate internal learnings from research and applied ML into scalable product workflows Develop systems that enable developers to customize and deploy models without needing ML expertise Collaborate closely with product engineers to define APIs, UX, and developer experiences Identify opportunities to automate or simplify what's currently done manually in applied ML projects Shape the foundation that powers future ML-driven products and applications What You'll Gain: The opportunity to bridge cutting-edge ML and real-world developer usability Deep collaboration with world-class ML researchers and engineers Influence over how Liquid's core technology reaches developers and enterprises A front-row seat in shaping how efficient AI becomes accessible at scale
About Liquid AI
Spun out of MIT CSAIL, we're a foundation model company headquartered in Boston. Our mission is to build capable and efficient general-purpose AI systems at every scale-from phones and vehicles to enterprise servers and embedded chips. Our models are designed to run where others stall: on CPUs, with low latency, minimal memory, and maximum reliability. We're already partnering with global enterprises across consumer electronics, automotive, life sciences, and financial services. And we're just getting started.
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.
While San Francisco and Boston are preferred, we are open to other locations in United States.
This Role Is For You If: You're an ML engineer who loves building for developers, not just models You get excited by turning research breakthroughs into usable platforms You want to make powerful model customization accessible to non-ML engineers You think in systems and abstractions-how to package complexity into simple APIs and flows You enjoy collaborating across research, infra, and product to bring technology into builders' hands Desired Experience: Strong applied ML experience (training, fine-tuning, evaluation, deployment) Experience building tools, SDKs, or infrastructure for developers Deep understanding of how to expose ML capabilities through clean abstractions Familiarity with model optimization, distillation, or on-device inference a plus Comfort translating research into practical, productized workflows What You'll Actually Do: Build and evolve Liquid's internal ML-to-developer platform layer Translate internal learnings from research and applied ML into scalable product workflows Develop systems that enable developers to customize and deploy models without needing ML expertise Collaborate closely with product engineers to define APIs, UX, and developer experiences Identify opportunities to automate or simplify what's currently done manually in applied ML projects Shape the foundation that powers future ML-driven products and applications What You'll Gain: The opportunity to bridge cutting-edge ML and real-world developer usability Deep collaboration with world-class ML researchers and engineers Influence over how Liquid's core technology reaches developers and enterprises A front-row seat in shaping how efficient AI becomes accessible at scale
About Liquid AI
Spun out of MIT CSAIL, we're a foundation model company headquartered in Boston. Our mission is to build capable and efficient general-purpose AI systems at every scale-from phones and vehicles to enterprise servers and embedded chips. Our models are designed to run where others stall: on CPUs, with low latency, minimal memory, and maximum reliability. We're already partnering with global enterprises across consumer electronics, automotive, life sciences, and financial services. And we're just getting started.