Robot Learning Engineer - Manipulation

applied; Sunnyvale (CA); On-site; USD 180,000 - 260,000; Full time

Job summary

Applied Intuition, Inc. in Sunnyvale seeks a Robot Learning Engineer to advance manipulation policies on real robots, from task definition to deployment.

You will train and refine policies, diagnose failures, and ensure reliable operation on customer lines with hands-on work alongside hardware. We welcome candidates with practical experience and strong Python/PyTorch skills, plus knowledge of imitation learning and robot kinematics.

Qualifications

• Trained or fine-tuned a learned manipulation policy and deployed on a physical robot.
• Strong Python and PyTorch skills for training, evaluation, and deployment.
• Experience with imitation learning and at least one modern policy family (vision-language-action, diffusion policies, or action-chunking transformers).
• Working knowledge of robot kinematics, coordinate frames, camera calibration, and interfaces between learned actions and low-level control.
• Habit of diagnosing failures with controlled experiments across data, sensing, model, and execution.

Responsibilities

• 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 to compact task-specific policies.
• Develop repeatable recipes for industrial tasks such as pick-and-place and assembly, adding force or tactile signals where helpful.
• Deploy policies on edge compute and validate observation processing, action interfaces, and control timing on the robot.
• Turn failures and interventions into better data, models, and evaluation.
• Measure success rate, cycle time, intervention rate, and data/time to reach targets, improving task-by-task.
• Package recipes and models so the next task starts from learned learnings.

Skills

Python

PyTorch

Imitation learning

Robot manipulation

Experiment design

Communication

Open-ended problems

Tools

ROS 2

LeRobot

Isaac

MuJoCo

NVIDIA Jetson

Job description

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 applied.co.

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

About the role

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.

At Applied Intuition, you will:

• 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.

We're looking for someone who has:

• 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 training, evaluation, and deployment code.
• Practical depth in imitation learning and at least one modern policy family, such as vision‑language‑action models, diffusion policies, or action‑chunking transformers.
• Working knowledge of robot kinematics, coordinate frames, camera calibration, and the interface between learned actions and low‑level control.
• The habit of diagnosing failures with controlled experiments across data, sensing, model, and execution.
• Comfort taking on open‑ended problems and communicating tradeoffs clearly to teammates at the robot.

Nice to Have:

• Experience with bimanual manipulation, force‑controlled insertion, tactile sensing, or dexterous hands.
• Experience with ROS 2, LeRobot, or simulators such as Isaac and MuJoCo.
• Experience optimizing inference on NVIDIA Jetson or GPU edge systems, including model export, compilation, or quantization.
• Experience with reinforcement learning post‑training, learning from interventions, or transferring policies across robot platforms.

Compensation at Applied Intuition for eligible roles includes base salary, equity, and benefits. Base salary is a single component of the total compensation package, which may also include equity in the form of options and/or restricted stock units, comprehensive health, dental, vision, life and disability insurance coverage, 401k retirement benefits with employer match, learning and wellness stipends, and paid time off. Note that benefits are subject to change and may vary based on jurisdiction of employment.

Applied Intuition pay ranges reflect the minimum and maximum intended target base salary for new hire salaries for the position. The actual base salary offered to a successful candidate will additionally be influenced by a variety of factors including experience, credentials & certifications, educational attainment, skill level requirements, interview performance, and the level and scope of the position.

Don’t meet every single requirement? If you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyway. You may be just the right candidate for this or other roles.

Applied Intuition is an equal opportunity employer and federal contractor or subcontractor. Consequently, the parties agree that, as applicable, they will abide by the requirements of 41 CFR 60-1.4(a), 41 CFR 60-300.5(a) and 41 CFR 60-741.5(a) and that these laws are incorporated herein by reference. These regulations prohibit discrimination against qualified individuals based on their status as protected veterans or individuals with disabilities, and prohibit discrimination against all individuals based on race, color, religion, sex, sexual orientation, gender identity or national origin. These regulations require that covered prime contractors and subcontractors take affirmative action to employ and advance in employment individuals without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status or disability. The parties also agree that, as applicable, they will abide by the requirements of Executive Order 13496 (29 CFR Part 471, Appendix A to Subpart A), relating to the notice of employee rights under federal labor laws.

FOR US-BASED ROLES: Applied Intuition is committed to providing an accessible and inclusive application and interview experience to applicants who are disabled veterans and other applicants with disabilities or medical conditions. Reasonable accommodations are available, requesting an accommodation will not affect your candidacy in any way, and you are not required to disclose the nature of your disability or medical condition in order to make a request. If you require an accommodation please contact [e-mail]. We will work with you!