Edge AI Engineer (human)
Confirmed on the employer's own hiring board on Oct 21, 2026. First seen by Alion on Oct 5, 2026. NEURA Robotics scores A on the Alion truth index.
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.
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).
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.
Optimize to the metal: Profile, analyze, and tune models and runtimes to meet the latency, power, and memory budgets of each hardware target.
Partner across functions: Work closely with Research, Hardware, Software, and Product to bring on-device AI from research to shipped product.
Set the bar for quality: Build and maintain benchmarking, on-device evaluation, and performance regression testing across every hardware target we ship.
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
Strong academic foundation: A Master's or PhD in Computer Science, Electrical Engineering, Embedded Systems, or a related field.
Proven experience: 3+ years in ML or embedded AI engineering, with real production deployment on embedded accelerators, not just papers.
Hardware fluency: Hands-on experience with embedded AI hardware platforms (e.g. NVIDIA Jetson, Qualcomm IQ-series, or comparable).
Optimization expertise: Solid knowledge of model optimization from the model side to the metal: quantization, pruning, distillation, and architecture search.
Toolchain expertise: Fluency with common model optimization and deployment toolchains (e.g. ONNX, TensorRT, AIMET, or equivalent vendor SDKs).
Technical depth: Strong Python and C++, PyTorch experience, and comfort with embedded Linux and low-level profiling.
The right mindset: A conviction that the only real test is the target chip, not the training cluster.
Collaboration & communication: The ability to work independently, make sound calls under uncertainty, and speak fluently to researchers, hardware engineers, and product alike. Professional English required; German is a strong plus (B2 to C1).