Senior/Staff AI Algorithms Engineer

Requirements PhD or equivalent experience in machine learning, robotics, optimization, applied math, or a related field

• Strong mathematical foundation in areas such as probability, statistics, optimization, and reinforcement learning
• Ability to turn theoretical insights into practical, testable code using Python and PyTorch (or similar frameworks)
• Curiosity, initiative, and a bias toward building and debugging in real systems
• Clear communication skills and an ability to collaborate across engineering and research boundaries
• (Desirable) Experience with physics simulators, control stacks, or physical sensing modalities (RGBD, force-torque, etc.)
• (Desirable) Prior work on real-world ML deployments (e.g., autonomous vehicles, logistics, hardware-integrated ML)
• (Desirable) Contributions to open-source ML, RL, or robotics libraries
• (Desirable) Previous startup experience

What the job involves We’re looking for an Senior/Staff AI Algorithms Engineer with deep foundations in machine learning, reinforcement learning, and optimization—and a strong drive to apply those skills to novel, high-impact problems in robotics

• You will leverage techniques from machine learning, reinforcement learning, search, and 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 enhance Dexterity’s technology foundations in Physical AI
• You’ll be part of a team working across simulation, real robots, and multimodal sensor streams, applying first-principles thinking to unlock product capabilities where off-the-shelf ML solutions fall short
• If you have strong research instincts but are excited to build things that are used in the real world, this role offers the chance to turn deep technical ideas into systems deployed at scale
• Design and prototype novel algorithms at the intersection of learning, prediction, and planning, with a focus on solving real product challenges
• Apply reinforcement learning, geometric reasoning, or optimization-based methods to tackle long-horizon manipulation, sensor fusion, or decision-making under uncertainty
• Translate open-ended problems into clear technical approaches, balancing modeling intuition with real-world constraints like latency, reliability, and partial observability
• Build and test your algorithms across simulation and real-robot environmentsCollaborate closely with product leads, software engineers, and roboticist to bring ideas into fruition
• Contribute to internal knowledge, tooling, and experimentation pipelines that level up the entire team
• You’ll work on hard, novel problems that demand first-principles thinking—not just tuning models
• Your work will run on physical systems with real constraints, feedback, and real world impact
• You’ll be expected to and supported in owning high-impact projects from early on
• You’ll have a chance to help shape how learning, prediction, and planning come together in next-generation robotics

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