Group Lead Edge AI (human)
LocationMetzingen / Riederich
Work modeon-site
Typefull-time
SeniorityLead
Experience7+ yrs
DepartmentAI
Company size1,001–5,000 people
PostedSep 21, 2026 · 3w ago
Verified live20h ago
At a glanceSummarised by Seekless from the posting.
Strong academic foundation: An excellent Master's or PhD in Computer Science, Electrical Engineering, Embedded Systems, or a related field.
7+ years in ML or embedded AI engineering, with 2+ years leading technically.
Real production deployment on embedded accelerators, not just papers.
Hands-on experience with embedded AI hardware platforms (e.g. NVIDIA Jetson, Qualcomm IQ-series, or comparable).
Deep expertise in model optimization: quantization, pruning, distillation, and architecture search.
Fluency with common model optimization and deployment toolchains (e.g. ONNX, TensorRT, AIMET, or equivalent vendor SDKs).
Strong Python and C++, PyTorch experience.
Comfort with embedded Linux and low-level profiling.
A conviction that the only real test is the target chip, not the training cluster.
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).
Skills
Python
C++
PyTorch
ONNX
TensorRT
AIMET
NVIDIA Jetson
Qualcomm IQ-series
embedded Linux
Your Mission & Challenges
About the role
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.
•
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.
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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.
•
The right mindset: A conviction that the only real test is the target chip, not the training cluster.
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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).