THE COMPANY
Our mission is to build the Covariant Brain, a universal AI to give robots the ability to see, reason and act on the world around them. Bringing AI from research in the lab to the infinite variability and constant change of our customer’s real-world operations requires new ideas, approaches and techniques.Success in the real world requires a team that represents that world: diversity of backgrounds, points of view, and experiences. Our common denominator: ambitious expectations, love of learning, empathy for those around us, and a team-first mindset.THE ROLEThe Covariant Brain is a Robotics Foundational Model that powers all robotic applications at Covariant.
The Brain is a collection of state-of-the-art models, algorithms, and APIs that enable all intelligent behavior of the robot, from perception to 3D object understanding, grasp sampling, ranking, motion planning, and control. The ML Performance Team owns the Brain models and algorithms used in production stations. For developing the next generation of our motion planning stack, we are looking for a candidate with extensive experience in designing and deploying motion planning algorithms in the real world.
YOU WILL
- Identify, design, and implement both short and long-term improvements to the Brain’s motion planning algorithms
- Balance performance tradeoffs (optimality, latency, memory usage) to deliver the best motion planner for Covariant’s robotic applications
- Apply software engineering best practices to the Brain’s motion planning API
- Serve as an expert on path planning, acceleration, collision checking, motion simulation, and robot control
- Collaborate with the software team on integrating motion planning improvements into robotic applications
- Support the hardware during the station design stage in the context of reachability and optimizing the expected robot motion time
- Be a full member of the ML Performance Team by supporting additional models and algorithms in the Brain as needed
- Travel to the customer site as needed to resolve issues with robots in production (not expected for more than once per quarter, if needed at all)
YOU HAVE
- Extensive experience in designing and implementing motion planning for applications that operate in the real world
- Expert-level knowledge of motion planning algorithms
- A track record of shipping well-designed, well-tested production code
- Mathematical maturity
- Demonstrated strong problem-solving ability: analyzing real-world problems and formulating solutions, iterating and formulating, shipping, and making an impact on products for customers
- Clear communication and collaboration across teams: taking requests from customers and PMs while prioritizing work across SW and HW teams
NICE TO HAVES
- Experience with 6 degrees of freedom (DoF) robots
- Proficiency in Python and experience with tensor libraries (e.g. NumPy, PyTorch, TensorFlow)
- Research experience in academia or industry
$170,000 - $225,000 a yearBase pay is one element of our total rewards package which may also include comprehensive benefits and equity etc., depending on eligibility. The actual base pay offered will be determined by factors such as years of relevant experience, skills, education, etc. Decisions will be determined on a case-by-case basis.
COMPANY CORE VALUES
LEARNING CONSTANTLY STRIVING FOR EMPATHY TAKING ON THE IMPOSSIBLE, TOGETHER
BENEFITS (US)
Health, dental, and vision insurance for you and your familyUnlimited PTO and Flexible work hours401(k) plan and company matchLunch and dinner each day (for on-site employees)Monthly Health & Wellness budgetQuarterly Learning budgetAt covariant.ai we don’t just accept difference—we celebrate it, we support it, and we thrive on it for the benefit of our employees, our products, and our community. Covariant.ai is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status.Apply for this job
