ML Engineer — Robotics

About the company:

KIEFER is building Greece's integrated AI ecosystem. From renewable energy infrastructure and AI systems to robotics and enterprise applications, we connect the technologies that power Europe's intelligent future. Founded in 2014, KIEFER has delivered 600MW+ of energy projects and is now developing sovereign AI infrastructure, enterprise AI products and physical AI systems for Greece and Southeast Europe.

We are looking for an ML Engineer to develop capabilities that work on physical robots. You will take ideas from model and data experiments through robotics integration and evaluation on real hardware.

Our fleet includes Unitree Go2, G1, H1, and B2 robots using NVIDIA Jetson Orin and Jetson Thor compute. Current areas of focus include navigation, learned policies, vision-language-action models, and robotics data.

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  Develop, train, adapt, and evaluate models for navigation, perception, planning, and robot behavior.

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  Apply suitable methods such as imitation learning, reinforcement learning, or vision-language-action approaches.

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  Build data collection and evaluation pipelines using real-world and simulated robotics data.

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  Integrate models with robotics software, sensors, actuators, and control systems.

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  Test on physical robots, measuring task success, sim-to-real performance, on-device latency, and failure modes.

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  Work with ML, Engineering, robotics specialists, and Product to improve reliability and move prototypes toward repeatable deployment.

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  3+ years of relevant ML or robotics engineering experience, or equivalent impact taking research into real-world systems.

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  Hands-on experience developing and evaluating ML-enabled systems on physical robots.

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  Strong Python and production software engineering skills, including experience with ROS or a comparable robotics framework.

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  A strong ML background in robot learning, perception, imitation learning, reinforcement learning, multimodal models, or a related area.

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  Experience with robotics data, simulation, experiment design, and evaluation on real hardware.

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  Practical understanding of sensors, actuators, control loops, latency, and hardware debugging.

• Vision-language-action models, multimodal foundation models, or generative world models.

• Experience with humanoids, mobile or dexterous manipulation, or other complex robotic platforms.

• Sim-to-real transfer, teleoperation, human demonstration data, or large-scale robotics data collection.

• C++, low-level control, tactile or force sensing, or real-time systems.

• Compensation: Competitive package aligned with talent benchmarks.

• Impact: Hands-on work shaping AI capabilities for Kiefer Tech’s products and real-world applications.

• AI-native environment: Real challenges across model development, evaluation, and production AI systems.

• Culture: Engineering-first, high autonomy, low bureaucracy, and space to shape meaningful AI products.