Robotics | Applied AI Engineer | Physical Systems

Fast paced, open & collaborative environment! about the company

Our client is an innovative technology lab operating at the cutting edge of advanced automation and applied artificial intelligence. They are developing next-generation intelligent systems designed to transform physical labor on a massive scale. This is a highly dynamic environment where rapid iteration is prioritized, allowing engineers to see their software directly drive real-world physical capabilities within days.

about the role

You will serve as a critical engineer at the intersection of machine learning and physical execution. Your core focus will be translating complex AI models into tangible, real-world actions.

• Design and train sophisticated machine learning policies for dynamic, real-world control systems.

• Conduct hands-on experiments to systematically debug and close the gap between simulated environments and physical reality.

• Build and maintain high-speed data pipelines and simulation environments that enable rapid iteration.

• Analyze system performance during deployments to drive continuous improvements back into the training loop.

• Collaborate closely with cross-functional engineering teams to integrate software with complex physical constraints.

skills and experience

• Proven experience in applied machine learning, specifically focusing on reinforcement or imitation learning.

• Demonstrated ability to deploy models onto actual physical systems, moving beyond theoretical simulations.

• Advanced proficiency in Python and familiarity with modern ML or physics-based frameworks.

• A highly empirical, debugging-oriented mindset with a focus on practical results that work in the real world.

• Bonus: A background in control theory, dynamics, or experience with domain adaptation and multi-axis physical systems.

To apply online please use the apply function, alternatively you may contact Evangeline.

(EA: 94C3609/ R24124002 )