Description
Senior / Staff AI Research Engineer, Real-Time Inference
Location: Milpitas, CA
Department: AI
Why RoboForce
RoboForce is an AI robotics company developing Physical AI–powered Robo-Labor for dull, dirty, and dangerous work. The company's robots are engineered for demanding industrial environments, with a focus on real-world deployment and scalability.
We are looking for a Senior / Staff AI Research Engineer, Real-Time Inference to make embodied AI practical on the edge. In this role, you will drive the full stack of model optimization — from CUDA kernel engineering to quantization and compression — to deploy high-performance AI models on edge compute platforms powering RoboForce robots in the field.
Responsibilities
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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.
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Implement CUDA-level optimizations including custom kernels, memory layout tuning, and hardware-aware graph compilation to minimize model latency.
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Apply and advance model compression techniques — quantization (INT8/FP16/INT4), pruning, distillation, and structured sparsity — to achieve production-grade throughput on constrained devices.
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Profile and debug end-to-end inference stacks using tools such as NSight, TensorRT, and Triton to identify and eliminate performance bottlenecks.
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Collaborate with ML research and robotics teams to co-design model architectures that meet real-time control-loop latency requirements.
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Establish benchmarking frameworks to evaluate model performance across latency, throughput, power consumption, and accuracy tradeoffs on target hardware.
Requirements
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Master's degree in Computer Science, Electrical Engineering, or related field with 4+ years of experience, or a PhD degree.
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Deep expertise in CUDA programming, GPU architecture, and low-level kernel optimization, including custom kernel authoring with tools such as Triton.
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Hands-on experience with model quantization, pruning, distillation, and deployment using frameworks such as TensorRT, ONNX Runtime, TVM, or Triton.
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Proficiency in C++ and Python; strong systems programming and performance profiling skills.
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Experience deploying ML models on edge or embedded hardware (e.g., NVIDIA Jetson, Orin, or equivalent ARM/GPU SoCs).
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Requires 5 days/week in-office collaboration with the teams.
Bonus Qualifications
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Familiarity with embodied AI models — VLA, multimodal transformers, or diffusion-based policies — and their inference characteristics.
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Familiarity with compiler-based optimization pipelines such as XLA, torch.compile, or MLIR for graph-level model acceleration.
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Understanding of robotics system constraints such as control-loop timing, sensor fusion latency, and memory bandwidth limits on edge SoCs.
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Publication or production work in efficient deep learning or on-device ML systems.
Benefits
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Competitive stock options/equity programs.
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Health, dental, and vision insurance, 401(k) plan.
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Visa sponsorship and green card support for qualified candidates.
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Lunches and dinners, a fully stocked kitchen, and regular team-building events.
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