Role Description

We are looking to recruit an exceptional Machine Learning Engineer - Robot Manipulation to design, implement, test, and deploy robot manipulation algorithms that enable assembly and material movement tasks.

In this role you will:

• Design and implement machine learning algorithms, with a focus on reinforcement learning (RL) and imitation learning (IL), to enable robotic manipulators to perform complex tasks in dynamic environments.
• Translate high-level objectives into machine learning problems and deploy robust, scalable models to real-world robotic systems.
• Integrate your ML solutions into existing robotics workflows, ensuring that models are performant in both simulated and real-world settings.
• Drive innovation by incorporating the latest research in machine learning into practical applications that push the boundaries of robotic manipulation.
• Take ownership of critical ML projects, seeing them through from conception to successful deployment.
• Collaborate across disciplines to ensure seamless integration of ML models and provide technical mentorship to junior engineers.

Qualifications

Must-have:

• MS or PhD in machine learning, computer science, robotics, or a related field.
• Strong practical experience in training and deploying machine learning models for real-world applications.
• Deep understanding of reinforcement learning (RL) and imitation learning (IL) and their application to robotics.
• Proficiency in programming languages and tools commonly used in machine learning (e.g., Python, PyTorch).
• Experience with data collection, preprocessing, and management in the context of training ML models.
• Self-starter attitude with strong ability to identify problems, prioritize them, then plan and execute working solutions.
• Enthusiasm for working in a fast paced startup environment and eagerness to support the team on a variety of topics.

Nice-to-have:

• Familiarity with robotic simulation environments (e.g., Gazebo, MuJoCo) and experience in sim-to-real transfer.
• Experience in:
• Designing and implementing reward functions for complex manipulation tasks.
• Developing models that can handle noisy, incomplete, or sparse data.
• Deployment of ML models to edge devices for real-time inference.
• Accelerating ML training processes using GPU, TPU, or other HW accelerators.
• Using reinforcement learning frameworks, e.g. Stable Baselines, RLlib, or similar.