Member Of Technical Staff - Machine Learning Researcher/Engineer, Performance Optimization Liquid AI, an MIT spin-off, is a foundation model company headquartered in Boston, Massachusetts. Our mission is to build capable and efficient general-purpose AI systems at every scale. Our goal at Liquid is to build the most capable AI systems to solve problems at every scale, such that users can build, access, and control their AI solutions. This is to ensure that AI will get meaningfully, reliably and efficiently integrated at all enterprises. Long term, Liquid will create and deploy frontier-AI-powered solutions that are available to everyone. We are looking for an ML Research/Engineer (Performance Optimization) to design and optimize our Liquid Foundation Models (LFMs) on GPUs. This is a highly technical role that will expose you to state-of-the-art foundation model technology. Desired Experience CUDA CUTLASS C/C++ PyTorch/Triton You're A Great Fit 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. 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.