About the job
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.
Responsibilities
Profile, optimize, and scale RL training runs to reduce iteration time
Integrate new optimization techniques as they emerge from the research community
Design and implement tools and environments that test the boundaries of model capabilities
Turn proof-of-concept ideas into robust training pipelines and best-in-class models
Qualifications
Minimum
Strong Python and PyTorch proficiency, with hands-on experience optimizing training pipelines
Hands-on experience with reinforcement learning and the ability to translate optimization techniques from theory into practical implementations
Track record of integrating research ideas into robust, maintainable code
Experience with frameworks like DeepSpeed, FSDP, or vLLM for efficient model training and inference
Experience working with data pipelines, including curation, validation, and analysis to support post-training objectives
Contributions to open-source machine learning projects
M.S. or Ph.D. in Computer Science, Electrical Engineering, Mathematics, or a related field
Preferred
No preferred qualifications listed.