Group Lead Edge AI (human)

Neura Robotics; Gemeindeverwaltungsverband Metzingen, Riederich; Vor Ort; EUR 120.000 - 165.000; Vollzeit

Zusammenfassung

Neura Robotics in Germany is seeking a Group Lead Edge AI to own the roadmap for on-device intelligence and to guide a team of edge AI engineers from research to shipped product.

You will lead model optimization and deployment on embedded accelerators (Jetson, IQ-series), shape toolchains with ONNX/TensorRT/AIMET, and collaborate across hardware, software and product to meet latency, power, and memory targets.

Qualifikationen

• 7+ years in ML or embedded AI engineering, with 2+ years leading technically.
• Strong academic foundation: Master's or PhD in CS, EE, Embedded Systems, or related.
• Hands-on with embedded AI hardware platforms (NVIDIA Jetson, Qualcomm IQ-series).
• Expertise in model optimization: quantization, pruning, distillation, and architecture search.
• Fluency with ONNX, TensorRT, AIMET, and vendor SDKs.
• Strong Python and C++, PyTorch, and embedded Linux; low-level profiling experience.
• Leadership and communication: able to lead engineers and discuss with researchers, hardware, and product; German a plus (B2-C1).

Aufgaben

• Own the roadmap: Drive on-device AI from research to shipped product.
• Build and lead the team: Hire, mentor, and grow edge AI engineers.
• Deploy at the edge: Run models on embedded accelerators with quantization, pruning, distillation.
• Own the toolchain end to end: ONNX, TensorRT, AIMET and the SDKs.
• Partner across functions: Collaborate with hardware, software and product teams.
• Set the bar for quality: Benchmark, on-device evaluation, and performance testing.
• Get hands-on when it counts: Debug accuracy drops, chase latency spikes.

Kenntnisse

Python

C++

PyTorch

Embedded Linux

Profiling

Quantization

Pruning

Distillation

Architecture search

Jetson

German language

Ausbildung

Master's or PhD in Computer Science / Electrical Engineering / Embedded Systems

Tools

ONNX

TensorRT

AIMET

Vendor SDKs

Jobbeschreibung

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.

• Own the roadmap: Drive on-device AI from research to shipped product.

• Build and lead the team: Hire, mentor, and grow a team of edge AI engineers into the best in the field.

• 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 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.

• 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.

• Set the bar for quality: Own benchmarking, on-device evaluation, and performance regression testing across every hardware target we ship.

• 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

• Strong academic foundation: An excellent Master's or PhD in Computer Science, Electrical Engineering, Embedded Systems, or a related field.

• Proven experience: 7+ years in ML or embedded AI engineering, with 2+ years leading technically. 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 mastery: Deep expertise in 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.

• Leadership & communication: The ability to lead engineers, make calls under uncertainty, and speak fluently to researchers, hardware engineers, and product alike. Professional English required; German a strong plus (B2 to C1).