• Develop and optimize inference pipelines for embodied AI models (VLA, perception, world models) targeting real-time execution on edge hardware such as NVIDIA Jetson platforms.
• Implement CUDA-level optimizations including custom kernels, memory layout tuning, and hardware-aware graph compilation to minimize model latency.
• Apply and advance model compression techniques — quantization (INT8/FP16/INT4), pruning, distillation, and structured sparsity — to achieve production-grade throughput on constrained devices.
• Profile and debug end-to-end inference stacks using tools such as NSight, TensorRT, and Triton to identify and eliminate performance bottlenecks.
• Collaborate with ML research and robotics teams to co-design model architectures that meet real-time control-loop latency requirements.
• Establish benchmarking frameworks to evaluate model performance across latency, throughput, power consumption, and accuracy tradeoffs on target hardware.

• Master's degree in Computer Science, Electrical Engineering, or related field with 4+ years of experience, or a PhD degree.
• Deep expertise in CUDA programming, GPU architecture, and low-level kernel optimization, including custom kernel authoring with tools such as Triton.
• Hands-on experience with model quantization, pruning, distillation, and deployment using frameworks such as TensorRT, ONNX Runtime, TVM, or Triton.
• Proficiency in C++ and Python; strong systems programming and performance profiling skills.
• Experience deploying ML models on edge or embedded hardware (e.g., NVIDIA Jetson, Orin, or equivalent ARM/GPU SoCs).
• Requires 5 days/week in-office collaboration with the teams.

• Familiarity with embodied AI models — VLA, multimodal transformers, or diffusion-based policies — and their inference characteristics.
• Familiarity with compiler-based optimization pipelines such as XLA, torch.compile, or MLIR for graph-level model acceleration.
• Understanding of robotics system constraints such as control-loop timing, sensor fusion latency, and memory bandwidth limits on edge SoCs.
• Publication or production work in efficient deep learning or on-device ML systems.

• Competitive stock options/equity programs.
• Health, dental, and vision insurance, 401(k) plan.
• Visa sponsorship and green card support for qualified candidates.
• Lunches and dinners, a fully stocked kitchen, and regular team-building events.

Compensation: Salary $185,000–$325,000 USD + Bonus + Equity