Liquid AI, Inc
Member of Technical Staff - ML Research Engineer, Performance Optimization
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.
This Role Is For You If: You have experience writing high-performance, custom GPU kernels for training or inference You have an understanding of low-level profiling tools and how to tune kernels with such tools You have experience integrating GPU kernels into frameworks like PyTorch, bridging the gap between high-level models and low-level hardware performance You have a solid understanding of memory hierarchy and have optimized for compute and memory-bound workloads You have implemented fine-grain optimizations for a target hardware, e.g. targeting tensor cores Desired Experience: CUDA CUTLASS C/C++ PyTorch/Triton What You'll Actually Do: Write high-performance GPU kernels for inference workloads Optimize alternative architectures used at Liquid across all model parameter sizes Implement the latest techniques and ideas from research into low-level GPU kernels Continuously monitor, profile, and improve the performance of our inference pipelines What You'll Gain: Hands-on experience with state-of-the-art technology at a leading AI company Deeper expertise in machine learning systems and performance optimization Opportunity to bridge the gap between theoretical improvements in research and effective gains in practice A collaborative, fast-paced environment where your work directly shapes our products and the next generation of LFMs
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.
This Role Is For You If: You have experience writing high-performance, custom GPU kernels for training or inference You have an understanding of low-level profiling tools and how to tune kernels with such tools You have experience integrating GPU kernels into frameworks like PyTorch, bridging the gap between high-level models and low-level hardware performance You have a solid understanding of memory hierarchy and have optimized for compute and memory-bound workloads You have implemented fine-grain optimizations for a target hardware, e.g. targeting tensor cores Desired Experience: CUDA CUTLASS C/C++ PyTorch/Triton What You'll Actually Do: Write high-performance GPU kernels for inference workloads Optimize alternative architectures used at Liquid across all model parameter sizes Implement the latest techniques and ideas from research into low-level GPU kernels Continuously monitor, profile, and improve the performance of our inference pipelines What You'll Gain: Hands-on experience with state-of-the-art technology at a leading AI company Deeper expertise in machine learning systems and performance optimization Opportunity to bridge the gap between theoretical improvements in research and effective gains in practice A collaborative, fast-paced environment where your work directly shapes our products and the next generation of LFMs
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.