Machine Learning Researcher/Scientist | Robotics

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Описание

Exciting time to be in a fast growing company Robotics at the forefront! about the company Our client is an applied R&D laboratory operating within the robotics and artificial intelligence industry. They focus on developing advanced open-source robotic platforms and building the comprehensive software stacks required to power them. Dedicated to making scalable automated labor economically viable, the organization builds rapidly across hardware, autonomy, and simulation domains to deploy solutions in the real world. about the role You will be responsible for developing the perception and learning algorithms that enable robotic systems to function effectively in physical environments. Your primary tasks will involve designing representation models for whole-body control and advancing predictive world model research. You will evaluate these models directly on physical hardware and collaborate closely with cross-functional teams to deploy robust behaviors outside of a laboratory setting. The position also requires staying current with broader AI literature and contributing to open-source initiatives. skills and experience Strong foundations in computer vision with a track record of handling uncurated, real-world visual data. Hands-on expertise in representation learning and self-supervised training methods. Proven end-to-end experience in model training, including managing data pipelines, debugging, and running comprehensive evaluations. Direct experience with sim-to-real transfer, actively migrating models from simulation onto physical hardware. High proficiency in Python or C++, alongside strong familiarity with robotic simulation environments. A broad understanding of the wider machine learning landscape, including large language models (LLMs). Familiarity with imitation learning, real-time control on embedded systems, or contact-rich manipulation is highly advantageous. To apply online please use the 'apply' function, alternatively you may contact Evangeline. (EA: 94C3609/ R24124002 )

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