Robot Learning Engineer - Manipulation

Applied Intuition, Inc. is powering the future of physical AI. Founded in 2017 and now valued at $15 billion, the Silicon Valley company is creating the digital infrastructure needed to bring intelligence to every moving machine on the planet. Applied Intuition services the automotive, defense, trucking, construction, mining and agriculture industries in three core areas: tools and infrastructure, operating systems, and autonomy.

Eighteen of the top 20 global automakers, as well as the United States military and its allies, trust the company’s solutions to deliver physical intelligence. Applied Intuition is headquartered in Sunnyvale, California, with offices in Washington, D.C.;
San Diego;
Ft. Walton Beach, Florida;
Ann Arbor, Michigan;
London;
Stuttgart;
Munich;
Stockholm;
Bangalore;
Seoul; and Tokyo. Learn more at

We are an in-office company, and our expectation is that full-time employees primarily work from their Applied Intuition office 5 days a week. However, we also recognize the importance of flexibility and trust our employees to manage their schedules responsibly. This may include occasional remote work, starting the day with morning meetings from home before heading to the office, or leaving earlier when needed to accommodate family commitments.

This in-office expectation does not apply to contractor positions

Applied Intuition is building a robot learning platform on Dana, its physical AI platform: the data infrastructure and training intelligence a company needs to make any robot learn an industrial task and keep improving it. The robotics team builds that platform and uses it to deliver robot autonomy on customer lines, training, evaluating, and deploying policies on real robots doing real industrial tasks.

Everyone on the team works hands-on with hardware and sees their work reach customers.

As a Robot Learning Engineer, you will work on manipulation policies from task definition to a policy running reliably on a physical robot. You will train and fine-tune policies, understand why they fail, and make them dependable enough for customer use. Success is measured on the robot, not only on offline benchmarks.

We are open to candidates at different experience levels who meet the requirements. We value demonstrated work on real systems, including equivalent practical experience, over a particular degree or title.

• Work on the full learning loop for manipulation tasks: task definition, demonstration collection, data curation, training, real-robot evaluation, and deployment.

• Train and fine-tune manipulation policies, from large pretrained models such as vision-language-action models to compact task-specific policies, and choose the right approach for each task.

• Develop repeatable recipes for industrial tasks such as pick-and-place, bimanual handling, and contact-rich assembly, adding force or tactile signals where they help.

• Deploy policies on edge compute and validate observation processing, action interfaces, and control timing on the robot.

• Turn failures and human interventions into better data, better models, and better evaluation.

• Measure what customers care about, including success rate, cycle time, intervention rate, and the data and time needed to reach a target, and improve those numbers task after task.

• Package recipes and models so the next task, and the next robot, starts from what was learned on the last one.

• 1+ years of professional experience

• A Bachelor's degree in Computer Science, Software Engineering, or equivalent

• Trained or fine-tuned a learned manipulation policy and deployed and evaluated it on a physical robot.

• Strong Python and PyTorch skills, with the ability to write maintainable…