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 embodied AGI.

You will build, train, evaluate, and deploy the systems that make embodied intelligence work on real robots. From models and data pipelines to evaluation infrastructure and cloud-to-edge deployment, you will close the loop between research and hardware.

What You Will Do

• Train and ship models for robotics and embodied intelligence, running experiments in simulation and on real devices.
• Build evaluation infrastructure and design benchmarks that measure what matters.
• Own the path from hardware capture to cleaned training data to deployed model.
• Build the deployment stack for robot fleets. Remote teleoperation, on-robot model distillation, cloud-to-edge inference, intervention detection, and observability.
• Optimize models for edge deployment where latency and compute budgets matter.
• Work directly with research and hardware teams to move ideas from notebook to robot.
• Use AI coding tools as a core part of your workflow.

Requirements

• Strong ML engineering background with hands-on robotics or embodied intelligence experience.
• Production experience with training, evaluation, deployment, and observability.
• Experience with PyTorch, JAX, or equivalent, with depth in computer vision, sensor fusion, sequence modeling, or imitation learning.
• Strong Python and working C++ knowledge. Comfortable with GPU, distributed, or edge systems.
• You have shipped code that ran on real hardware.
• High agency - you don't wait to be told what to do.
• Fluent in English.

Nice to Have

• Experience with motion capture, IMU data, or visual-inertial fusion.
• Edge-ML deployment with TensorRT, ONNX, CoreML, TVM, or similar.
• Published research, strong open-source contributions, or shipped products at scale.

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