Senior Software Engineer (Robotic Packing/Manipulation)

• 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
• 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

Benefits

• Healthcare - Full medical, dental and vision. Life insurance, gym stipends and wellness programs
• Vacation - We value hard work and we like to have fun. Take time when you need it
• Recognition - Peer shout outs, achievement celebrations and leadership awards let you know you’ve made a difference
• Retirement - Stock options and generous 401(k) matching
• Nutrients - Lunch is delivered daily, a plethora of snacks, gourmet coffee, Soylent, and top-shelf beverages- Preferred Qualifications:
• 4+ years of experience in software development with a focus on robotics manipulation or related areas
• Experience with data science tools & libraries like numpy, pandas, scipy, matplotlib, scikit-learn
• Determine and communicate justification of technical priorities
• Experience with major deep learning frameworks such as PyTorch
• Experience in working with ROS or ROS2
• Strong development expertise in Python and C++
• Develop on and troubleshoot real robotic systems
• Master’s degree in Robotics, Machine Learning, Computer Vision, Computer Science or a closely related field
• 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
• Demonstrated experience training and adapting of existing machine learning architectures for robot learning, to solve tasks in domains such as manipulation, locomotion, or navigation
• Work independently on a variety of projects while maintaining focus
• Mentor junior engineers
• Demonstrated ability to:
• Work in a fast-paced environment with changing priorities
• Rapidly prototype and iterate on solutions to challenging problems
• 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
• Sim-to-real for robotic manipulation
• Applying machine learning to hardware interacting with the real world
• Real and simulated data capture
• 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)
• Docker, cloud computing, or similar applications
• Combining model-based and data-driven approaches
• 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