Senior​/AI Research Engineer, Inference

Senior / Staff AI Research Engineer, Real-Time Inference

Milpitas, CA

• 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, Tensor
  
  RT, 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 Tensor
  
  RT, 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.