Senior/Principal Machine Learning Engineer, Manipulation, R&D

• Master’s degree in Robotics, Machine Learning, Computer Vision, Computer Science or a closely related field.
• 4+ years of experience in software development with a focus on robotics manipulation or related areas.
• Strong development expertise in Python and C++
• Experience with major deep learning frameworks such as PyTorch
• Experience with data science tools & libraries like numpy, pandas, scipy, matplotlib, scikit-learn
• Experience in working with ROS or ROS2
• Demonstrated experience training and adapting of existing machine learning architectures for robot learning, to solve tasks in domains such as manipulation, locomotion, or navigation
• Demonstrated ability to: Develop on and troubleshoot real robotic systems Determine and communicate justification of technical priorities Rapidly prototype and iterate on solutions to challenging problems Work independently on a variety of projects while maintaining focus Work in a fast-paced environment with changing priorities Mentor junior engineers
• Develop on and troubleshoot real robotic systems
• Determine and communicate justification of technical priorities
• Rapidly prototype and iterate on solutions to challenging problems
• Work independently on a variety of projects while maintaining focus
• Work in a fast-paced environment with changing priorities
• Mentor junior engineers
• Strong verbal and written communication skills, capable of explaining complex ideas clearly and concisely to both technical and non-technical stakeholders
• Provide technical leadership on key projects

• PhD in Robotics, Machine Learning, Computer Science or a closely related field.
• Demonstrated technical proficiency in robot manipulation learning, such as reinforcement learning or imitation learning
• Experience with: Applying machine learning to hardware interacting with the real world Real and simulated data capture Sim-to-real for robotic manipulation Real-time perception-based control Incorporating tactile sensor information into manipulation behaviors Robot simulators (e.g. Isaac Sim or MuJoCo) Robot learning frameworks (e.g. Isaac-Gym, Orbit / Isaac Labs) Combining model-based and data-driven approaches Docker, cloud computing, or similar applications Experiment tracking and dataset management (e.g. Weights & Biases) Database systems as data source, such as MongoDB Parallel/distributed systems and asynchronous/concurrent programming
• Applying machine learning to hardware interacting with the real world
• Real and simulated data capture
• Sim-to-real for robotic manipulation
• Real-time perception-based control
• Incorporating tactile sensor information into manipulation behaviors
• Robot simulators (e.g. Isaac Sim or MuJoCo)
• Robot learning frameworks (e.g. Isaac-Gym, Orbit / Isaac Labs)
• Combining model-based and data-driven approaches
• Docker, cloud computing, or similar applications
• Experiment tracking and dataset management (e.g. Weights & Biases)
• Database systems as data source, such as MongoDB
• Parallel/distributed systems and asynchronous/concurrent programming
• Knowledge of LLM usage within robotics context

Possessing some of these preferred qualifications is great to have but not required and should not discourage applicants from applying.