As a Senior/Staff Algorithms Engineer, you will be working on a myriad of challenges related to robot task and action planning. You will leverage techniques from machine learning, search, discrete and continuous optimization to solve hard sequential decision problems that require reasoning about the physical world and its dynamics. You will also stay abreast of the latest progress in reinforcement learning, task and motion planning, and other related fields in order to further develop Dexterity’s technology foundations in physical AI.
Dexterity’s robotic solutions integrate data from a multitude of sensors, including RGB cameras, depth sensors, force-torque sensors, encoders, system telemetry and human input. To better inform the planning algorithms, you may work on sensor fusion and state estimation techniques to leverage this multimodal sensory data.
You will also work closely with the data platform, physics simulation, and robot operations teams to develop effective and efficient ways to improve the system’s internal world model.
Dexterity has an expanding set of algorithmic challenges as we deploy new robotic applications, including areas such as, Improving packing algorithms to build taller, denser, more stable structures with a wider variety of objects, Solving the logistics task of moving and sorting inventory throughout a warehouse, building models that understand physics and geometry for both short- and long-horizon tasks
In addition to core algorithmic developments, you will be responsible for building high quality, maintainable, and performant software. Ideally, you will bring strong software system design sensibilities and help guide software development within the team more broadly.
About Dexterity:
At Dexterity, we believe robots can positively transform the world. Our breakthrough technology frees people to do the creative, inspiring, problem-solving jobs that humans do best by enabling robots to handle repetitive and physically difficult work.
We’re starting with warehouse automation, where the need for smarter, more resilient supply chains impacts millions of lives and businesses around the world. Dexterity's full-stack robotics systems pick, move, pack, and collaborate with human-like skill, awareness, and learning capabilities. Our systems are software-driven, hardware-agnostic, and have already picked over 15 million goods in production. And did we mention we’re customer-obsessed? Every decision, large and small, is driven by one question – how can we empower our customers with robots to do more than they thought was possible?
Dexterity is one of the fastest growing companies in robotics, backed by world-class investors such as Kleiner Perkins, Lightspeed Venture Partners, and Obvious Ventures. We’re a diverse and multidisciplinary team with a culture built on passion, trust, and dedication. Come join Dexterity and help make intelligent robots a reality!
Day to Day Activities:
Make both revolutionary and evolutionary modifications to various algorithms spanning from modern optimal control to Go solversCollaborate with roboticist, machine learning engineers, mechanical engineers, and product managers to find optimal system solutionsImplement, test, and maintain high quality softwareDevelop and maintain accurate models of robotics systemsIntegrate algorithms with existing software infrastructure, including development of any necessary auxiliary softwareLead efforts to improve the rigor and performance of algorithms within Dexterity
Required Skills:
Degree in Computer Science, Electrical Engineering, or Mathematics and strong knowledge of Modern C++ and Python (preferred)5+ years of relevant Industry or Research experienceFamiliarity with Linux and git versioningExperience with Optimization (convex, continuous, combinatorial, etc.)Experience with various Search Strategies Monte Carlo Tree Search (MCTS), Depth-First Search (DFS), etc.Constraint Satisfaction Problems, Statistics and State EstimationSystem Modeling, Task and Motion Planning
Nice to haves:
Experience with Docker and KubernetesPrevious startup experience
