Edge AI Engineer
Posted 5 Oct 2026
Advertised as “Edge AI Engineer (human)”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 3+ yrs
- Management level
- Individual contributor
- Employment
- On-Site
- Contract
- Full-time
No salary listed for this role.
Field of studyComputer Science, Electrical Engineering, Embedded Systems (or related)
Study levelMaster's or higher
Your mission & challenges
As Edge AI Engineer, you turn cutting-edge models into production-grade, on-device intelligence for our robots. You work hands-on at the intersection of machine learning, embedded systems, and robotics. Your focus is making models fast, lean, and reliable on the hardware we ship.
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Deploy at the edge: Take models from trained to deployed using quantization, pruning, distillation, and every trick it takes to make them fast and lean on embedded accelerator platforms (e.g. NVIDIA Jetson, Qualcomm IQ-series).
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Own the toolchain: Work across model export and inference optimization frameworks (e.g. ONNX, TensorRT, AIMET) and the SDKs that turn a model into a working robot behavior.
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Optimize to the metal: Profile, analyze, and tune models and runtimes to meet the latency, power, and memory budgets of each hardware target.
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Partner across functions: Work closely with Research, Hardware, Software, and Product to bring on-device AI from research to shipped product.
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Set the bar for quality: Build and maintain benchmarking, on-device evaluation, and performance regression testing across every hardware target we ship.
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Solve the hard problems: Debug an accuracy drop after quantization, chase down a latency spike, and tackle the problems that only show up on the real chip.
What we can look forward to
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Strong academic foundation: A Master's or PhD in Computer Science, Electrical Engineering, Embedded Systems, or a related field.
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Proven experience: 3+ years in ML or embedded AI engineering, with real production deployment on embedded accelerators, not just papers.
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Hardware fluency: Hands-on experience with embedded AI hardware platforms (e.g. NVIDIA Jetson, Qualcomm IQ-series, or comparable).
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Optimization expertise: Solid knowledge of model optimization from the model side to the metal: quantization, pruning, distillation, and architecture search.
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Toolchain expertise: Fluency with common model optimization and deployment toolchains (e.g. ONNX, TensorRT, AIMET, or equivalent vendor SDKs).
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Technical depth: Strong Python and C++, PyTorch experience, and comfort with embedded Linux and low-level profiling.