Edge AI Engineer (human)

Neura Robotics; München; Hybrid; EUR 85,000 - 120,000; Full time

Job summary

Neura Robotics is seeking an Edge AI Engineer to turn cutting-edge models into production-grade, on-device intelligence for our robots. You will work at the intersection of ML, embedded systems, and robotics, focusing on fast, lean models on our hardware.

You will optimize models for edge deployment, collaborate with Research, Hardware, Software, and Product teams, and build robust benchmarking and testing for all hardware targets we ship.

Qualifications

• Master's or PhD in CS, EE, or embedded systems.
• 3+ years of ML or embedded AI engineering with production deployment.
• Hands-on with embedded AI hardware platforms.
• Strong Python and C++, PyTorch experience.
• Fluent English; German a plus.

Responsibilities

• Deploy at the edge with quantization, pruning, and distillation.
• Own the toolchain across export and inference optimization.
• Optimize to the metal for latency, power, memory budgets.
• Collaborate with Research, Hardware, Software, and Product.
• Set benchmarking and on-device evaluation standards.
• Debug issues like accuracy drops after quantization.

Skills

Python

C++

PyTorch

Embedded systems

Model quantization

Profiling

Education

Master's degree in CS/EE/Embedded

PhD in related field

Tools

ONNX

TensorRT

AIMET

Embedded Linux

Job description

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).