An application made for this job — a tailored resume and cover letter that speak straight to the posting.
Berkshire Grey is seeking a highly capable robotics software engineer to develop learning-based approaches for spatial reasoning, sequential decision-making, and robotic manipulation. You will work on perception, planning, and manipulation to enable reliable real-world performance under uncertainty, with both data-driven and model-based methods.
This role offers exposure to cutting-edge robotics, simulation-based evaluation, and direct impact on products for customers, with opportunities to
4+ years of experience in software development with a focus on robotics manipulation or related areasExperience with data science tools & libraries like numpy, pandas, scipy, matplotlib, scikit-learnDetermine and communicate justification of technical prioritiesExperience with major deep learning frameworks such as PyTorchExperience in working with ROS or ROS2Strong development expertise in Python and C++Develop on and troubleshoot real robotic systemsMaster’s degree in Robotics, Machine Learning, Computer Vision, Computer Science or a closely related fieldStrong verbal and written communication skills, capable of explaining complex ideas clearly and concisely to both technical and non-technical stakeholdersProvide technical leadership on key projectsDemonstrated experience training and adapting of existing machine learning architectures for robot learning, to solve tasks in domains such as manipulation, locomotion, or navigationWork independently on a variety of projects while maintaining focusMentor junior engineersDemonstrated ability to:Work in a fast-paced environment with changing prioritiesRapidly prototype and iterate on solutions to challenging problemsPhD in Robotics, Machine Learning, Computer Science or a closely related fieldDemonstrated technical proficiency in solving spatial reasoning and sequential decision-making in roboticsApplying machine learning to hardware interacting with the real worldSim-to-real for robotic manipulationReal and simulated data captureReal-time perception-based controlIncorporating tactile sensor information into manipulation behaviorsRobot simulators (e.g. OpenRAVE, Isaac Sim or MuJoCo)Robot learning frameworks (e.g. Isaac-Gym, Orbit / Isaac Labs)Docker, cloud computing, or similar applicationsCombining model-based and data-driven approachesExperiment tracking and dataset management (e.g. Weights & Biases)Database systems as data source, such as MongoDBParallel/distributed systems and asynchronous/concurrent programmingKnowledge of LLM usage within robotics context