AI Research Engineer

About the role

Join our startup as an AI Research Engineer, where you'll develop and optimize AI models and training systems for cutting-edge AI/robotics applications. You'll work closely with our engineering team to design, implement, and iterate on large-scale AI models while building efficient systems for rapid experimentation and deployment. This role requires a strong background in ML planning within the autonomous vehicle space and experience with the latest techniques in diffusion and autoregressive models. Key missions: Design and implement state-of-the-art machine learning models and training pipelines, applying novel techniques to a wide range of robotics applications.. Lead model development and iteration with a focus on rapid experimentation, performance optimization, and model debugging.. Collaborate with the engineering team to optimize training infrastructure and deployment, ensuring efficient systems for rapid experimentation and deployment. Profile: - This role requires a proven background working on ML planning within the autonomous vehicle space with experience using the latest techniques in diffusion and autoregressive models in a professional setting - If you're excited about building the latest advancements in AI and working in the robotics space, we’d love to hear from you - Have a strong understanding of modern ML architectures and training techniques - Have a background in implementing ML research papers and adapting academic work - Are familiar with training inference and infra pipelines that go from camera input to trajectory output for self driving or robotics - Have experience with RL (reinforcement learning) - Have 5+ years of AI modeling experience, specifically within the self-driving car industry (or PhD with 3+ years of AI modeling experience in the self-driving car industry) - Proven track record developing and deploying large-scale ML models - Have experience with model debugging, optimization, and performance tuning - Have experience using the latest techniques in diffusion and autoregressive models in a professional setting