Senior / Staff AI Research Engineer, Real-Time Inference

RoboForce•Milpitas, CA

2d•$185,000 - $325,000•Onsite

About The Position

RoboForce is an AI robotics company developing Physical AI-powered Robo-Labor for dull, dirty, and dangerous work. Its robots are designed for demanding industrial environments, with a focus on real-world deployment and scalability. The Senior / Staff AI Research Engineer, Real-Time Inference will make embodied AI practical on the edge by driving model optimization across CUDA kernel engineering, quantization, and compression. The role focuses on deploying high-performance AI models on edge compute platforms powering RoboForce robots in the field. It requires five days per week of in-office collaboration with the teams.

Requirements

Master's degree in Computer Science, Electrical Engineering, or a 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, with strong systems programming and performance profiling skills.
Experience deploying ML models on edge or embedded hardware, such as NVIDIA Jetson, Orin, or equivalent ARM/GPU SoCs.
Availability for five days per week of in-office collaboration with the teams.

Nice To Haves

Familiarity with embodied AI models—including 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.

Responsibilities

Develop and optimize inference pipelines for embodied AI models, including VLA, perception, and world models, for 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—including 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.