Senior/Staff AI Algorithms Engineer

Redwood City,Redwood CityFTsenior

Data ScienceOtherEngineer

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Quick Summary

Overview

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.

Technical Tools

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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 worldwide. Dexterity's full-stack robotics systems pick, move, pack, and collaborate with human-like skill, awareness, and learning capabilities. Our systems are software-driven and hardware-agnostic and have already picked 100+ 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!

About the Role

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

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, reinforcement learning, imitation learning, and foundation models

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

Experience with physics simulators, control stacks, or physical sensing modalities (RGBD, force-torque, etc.)

Prior work on real-world ML deployments (e.g., autonomous vehicles, logistics, hardware-integrated ML)

Contributions to open-source ML, RL, or robotics libraries

Previous startup experience

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