Group Lead Edge AI
Posted 21 Sep 2026
Advertised as “Group Lead Edge AI (human)”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 7+ yrs
- Management level
- People management
- 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 Group Lead Edge AI, you own the team that turns cutting-edge models into production-grade, on-device intelligence. You set the technical direction, make the hard calls on hardware and architecture, and stay hands-on enough to dive into a profiler when things get tough.
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Own the roadmap: Drive on-device AI from research to shipped product.
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Build and lead the team: Hire, mentor, and grow a team of edge AI engineers into the best in the field.
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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 end to end: Cover 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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Partner across functions: Work with Hardware, Software, and Product to push the limits of what fits in the latency, power, and memory budget you're given.
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Set the bar for quality: Own benchmarking, on-device evaluation, and performance regression testing across every hardware target we ship.
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Get hands-on when it counts: Debug an accuracy drop after quantization, chase down a latency spike, solve the problem nobody else can.
What We Can Look Forward To
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Strong academic foundation: An excellent Master's or PhD in Computer Science, Electrical Engineering, Embedded Systems, or a related field.
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Proven experience: 7+ years in ML or embedded AI engineering, with 2+ years leading technically. 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 mastery: Deep expertise in 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).