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 )
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