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Applied Intuition

Builds software for autonomous vehicles

Robot Learning Engineer - Manipulation

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeSunnyvale, CA, USA

About the Job

Requirements

  • At least 1 year of professional experience.
  • A Bachelor's degree in Computer Science, Software Engineering, or equivalent.
  • Have trained or fine-tuned a learned manipulation policy and deployed and evaluated it on a physical robot.
  • Strong Python and PyTorch skills, including 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.
  • Ability to diagnose 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.

Responsibilities

  • Work on the full learning loop for manipulation tasks, including 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 customer-relevant outcomes, including success rate, cycle time, intervention rate, and the data and time needed to reach a target, and improve those outcomes task after task.
  • Package recipes and models so subsequent tasks and robots can build on prior learning.

Desired Qualifications

  • 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.

About the Company

Applied Intuition

Builds software for autonomous vehicles

Applied Intuition builds software for automakers and operators developing autonomous vehicles and other machines, from passenger cars to industrial equipment. Its Tools for Vehicle Intelligence let engineers test driving behavior in simulated worlds, while Vehicle OS connects software inside a machine with development tools off the vehicle. The Self-Driving System supports driver assistance and fully driverless operation for passenger and commercial vehicles. It sells these systems to businesses and works alongside their engineers to integrate, validate and update vehicle software. Applied Intuition was founded in 2017 and is headquartered in Sunnyvale, California.

Company size
1,001-5,000
Company stage
Series F
Total funding
$1.2B
Headquarters
Sunnyvale, CA
Founded
2017

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