Senior / Staff AI Research Engineer, Real-Time Inference

Milpitas, CAfull timeOn Site

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senior

$185k–$325k

H1B: yes

Roboforce Senior / Staff AI Research Engineer, Real-Time Inference at Roboforce responsible for optimizing AI model inference pipelines for real-time execution on edge hardware. The role involves CUDA kernel engineering, model compression, and performance profiling to deploy AI models on robotics platforms.

Responsibilities

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.

Qualification

Master's degree in Computer ScienceDeep expertise in CUDA programmingProficiency in C++ and PythonBonus Qualifications

Required

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.
Bonus Qualifications
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.

Preferred

Familiarity with embodied AI models such as VLA, multimodal transformers, or diffusion-based policies
Familiarity with compiler-based optimization pipelines like XLA, torch.compile, or MLIR
Understanding of robotics system constraints including control-loop timing and sensor fusion latency
Publication or production work in efficient deep learning or on-device ML systems

Benefits

Dental
Vision
401(k)
Equity/RSU

Company

Roboforce

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Source: greenhouse

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Must-Have Skills for This Role

CUDATensorRTTritonONNX RuntimeTVMC++

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Senior / Staff AI Research Engineer, Real-Time Inference at Roboforce · LumiJobs