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Relari, inc. in San Francisco develops machine learning systems for dexterous robotic manipulation. We seek engineers who turn research ideas into working systems, spanning data processing, training, evaluation, and real-world deployment.
You will design and optimize training pipelines, build scalable infrastructure, and collaborate with robotics engineers to deploy models on physical robots using feedback from real-world use.
You will develop machine learning systems that enable robots to acquire dexterous manipulation skills from human demonstrations and multimodal sensor data. You will work across data processing, model training, evaluation, and real-world deployment, using experiments to understand what makes robots learn more effectively. Our research involves vision, motion, biomechanical signals, and robot interaction data, with applications to imitation learning, vision-language-action models, video models, and world models. We are looking for engineers with strong machine learning fundamentals and the ability to turn research ideas into working systems. Prior robotics experience is not required.