Develop learning-based robotics software for spatial reasoning, sequential decision-making, perception, planning, and manipulation. Build and evaluate models in simulation and on physical robotic systems, prototype solutions, improve reliability under uncertainty, and transition successful methods into products. The role includes collaborating with research partners, mentoring junior engineers, communicating technical priorities, and applying machine learning, real-time control, sensor data, and sim-to-real techniques to robotic packing and manipulation.
About The Job
Berkshire Grey is a leader in the field of AI and robotics, providing innovative solutions for e-commerce, retail replenishment, and logistics. Our technology automates complex pick, pack, and sort operations.
As part of the Core software team, you will develop learning-based approaches that help robots reason about 3D environments, anticipate the outcomes of physical interactions, and make effective decisions over sequences of actions. The work combines perception, planning, and manipulation, with an emphasis on reliable performance under uncertainty and real-world constraints. You’ll explore both data-driven and model-based approaches, evaluate them in simulation, and bring promising methods onto physical robotic systems.
Your work will contribute to enhancing the capabilities of our robotic solutions and explore new solutions and approaches, unlocking new value to be delivered to our customers. This position offers a unique opportunity to work at the cutting edge of robotics applied to real-world challenges.
Responsibilities
- Develop learning methods for spatial reasoning, sequential decision-making, and robotic manipulation.
- Combine learned policies and predictive models with planning and optimization to solve physically constrained tasks.
- Design training and evaluation workflows that measure task success, robustness, and execution efficiency.
- Quickly prototype solutions, creating demos for stakeholders and visitors.
- Serve as subject matter for transitioning prototypes to product teams.
- Identify high impact areas for improvements of our robotic systems to solve real problems.
- Utilize and extend simulation software environments to develop and test manipulation behaviors.
- Stay abreast of the latest advancements in robotics and related fields, evaluating applicability to our challenges.
- Collaborate with external research partners to find solutions for BG’s problem space.
- Assist with mentorship of more junior engineers or interns.
- Communicate technical priorities and status.
Minimum Qualifications:
- 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
- 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
Preferred Qualifications:
- PhD in Robotics, Machine Learning, Computer Science or a closely related field.
- Demonstrated technical proficiency in solving spatial reasoning and sequential decision-making in robotics.
- 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. OpenRAVE, 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.
6110-2609BC
Berkshire Grey Bedford, Massachusetts, USA Office
140 South Road, Bedford, MA, United States, 01730
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