Edge AI Engineer; human

• NEW

Neura Robotics
• Riederich

In the AI Department, you'll develop the cognitive abilities of our robots, enabling them to understand and interact with their surroundings. You'll work on algorithms that make machines intelligent and adaptive—ranging from object recognition and language processing to decision-making in complex environments. You'll be part of a team that pushes technological boundaries and collaborates closely with software, hardware, and product management teams.

Using cutting‑edge tools and methods from machine learning and AI, you'll work on projects that have a direct impact on our products. If you're excited about making machines smarter and shaping the future of human‑machine interaction, the AI Department is the perfect place for you.

• Full-time

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.

• 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 tool chains (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).

Enjoy a dynamic, self-reliant work culture with flat hierarchies, flexible hours, and 30 vacation days. Ideal for those seeking an inspiring professional setting, whether you're starting out or an experienced exec.

A passionate and highly skilled team of international experts aiming to redefine robot assistants.

Enjoy a competitive salary package…