ML Engineer

The next ten years of AI will not be won in software. They will be won in the physical world. In factories, hospitals, kitchens, fields, and homes. The companies that own that data will own the century.

MicroAGI is building it. We are the data layer for physical AI.

What You Will Do

• Work as a generalist ML engineer across the stack - train and ship models, design data pipelines, run experiments, debug production behavior. Whatever the team needs, not one narrow specialty.
• Own data pipelines end-to-end: from hardware capture → cleaned training set → deployed model.
• Run experiments on real devices, not just in simulation. Close the sim-to-real gap iteratively.
• Optimize models for edge deployment when latency and compute budgets matter.
• Partner closely with Hardware/IoT engineers to spec what data we need, from which sensors, at which rate — because the ML is only as good as what comes off the sensor.

Requirements

• Location: Munich
• Full-time
• Up to 5 years of production ML / deep-learning experience. PyTorch, JAX, or equivalent.
• Solid foundation in at least one of: computer vision, sensor fusion, sequence modeling, or imitation learning.
• Comfortable in hardware-adjacent territory — you've touched embedded systems, real-time data, sensors, or edge inference. You're not afraid of a serial port.
• Strong Python. C++ familiarity a plus.
• High agency. You don't wait for a clean spec; you build, measure, iterate.
• Fluent business English. German is a plus.
• Discreet. You handle confidential model behavior and customer data well.

Nice to Have

• Experience with motion capture, IMU data, or visual-inertial fusion.
• Edge-ML deployment: TensorRT, ONNX, CoreML, TVM, or similar.
• Robotics, imitation learning, or world-models background.
• Embedded-systems or firmware experience — even a hobby level.
• Published research, strong open-source contributions, or shipped products at scale.