NeuralNetworkPerformanceEngineer

Here at Humanoid, we believe in a future where robots amplify human potential. That’s why we’ve set out on a mission to build the world’s most capable, commercially-scalable, and safe humanoid robots. We’re bringing that mission to life with HMND‑01 Alpha - our rapidly developed humanoid platform now running in real industrial pilots - and we’re growing the team to take it even further. ABOUT THE ROLE We're hiring a Neural Network Performance Engineer to join our VLA team based in London. In this role, you will work on all aspects of running capable neural-network based control policies at a high rate with minimal latency, both on cloud hardware and onboard. Your work will be critical to delivering smooth robot motions while reacting to environment changes as quickly as possible. WHAT YOU'LL DO - Analyze performance bottlenecks of a particular model architecture and come up with potential improvements. - Make the model run on a new hardware (e.g. NVIDIA Thor) efficiently. - Implement custom kernels to reduce memory throughput requirements where it matters. - Quantize a model with minimal loss of quality. - Suggest and implement changes of model architecture that will enable better performance characteristics without sacrificing model capabilities. WHAT WE'RE LOOKING FOR - 3+ years building deep‑learning systems (industry or research) with shipped models or published artifacts to show for it. - 1+ years experience working on performance of neural network inference (analyzing bottlenecks, writing custom kernels, quantizing models, fighting deep learning compilers). - Excellent understanding of GPU architecture and why some models run faster than others. - Strong Python + PyTorch/JAX; you can profile, debug numerics, and write maintainable research code. - You document experiments clearly and communicate trade‑offs crisply. Nice to have: - Robotics or autonomous driving experience. - Open source code showcasing your ability to improve inference performance. - Publicati