1 month ago

AI Researcher - Reinforcement Learning

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Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 17 hours agoStatus: Live

Job Summary

Own the full reinforcement learning pipeline for a humanoid home robot, from algorithm development and simulation training to closing the sim-to-real gap and deploying reliable policies in real-world environments. Train and deploy skills for manipulation and locomotion, build training/evaluation infrastructure, and partner with hardware, controls, data collection, and QA teams to ship and improve policies based on field task success.

Location: United States

Workplace: Onsite

Employment Type: Full time

Job Function: Research & Scientific (R&D)

Key Responsibilities

•Own the end-to-end RL pipeline from algorithm development through production deployment for manipulation and locomotion tasks.
•Train policies in simulation, close the sim-to-real gap, and ship skills that work reliably in real-world home environments.
•Measure impact using field task success rates and iterate based on what NEO can do in the field.
•Build training and evaluation infrastructure with standardized benchmarks, automated regression detection, and links between training metrics and field performance.
•Collaborate with hardware, controls, data collection, and QA teams to take RL-trained skills to production customer sites.

Pay and Benefits

Salary: USD 200,000 - 300,000 annually

Equity and Bonus:Equity

Perks:Health InsuranceDentalVision401kParental LeavePaid Leave

Key Requirements

•Strong Python and/or C++ experience in large codebases and build tools (Bazel or equivalent).
•Proficiency with PyTorch for reinforcement learning policy training and experimentation.
•Hands-on experience with simulation platforms (Isaac Sim, MuJoCo, or equivalent) for large-scale policy training.
•Experience training RL policies for manipulation or locomotion tasks, including addressing the sim-to-real gap on physical hardware.
•Background with model-based RL or world-model-guided policy learning and/or imitation learning (behavior cloning, GAIL, IQL).

Experience:RoboticsReinforcement learningSim-to-realSimulationPhysical robotics

Skills:Cross-functional collaborationTechnical communicationOwnershipProblem-solvingIteration

Tech Stack:PythonC++BazelPyTorchIsaac SimMuJoCoPPOSACTD-MPCDomain randomizationReward shapingBehavior cloningGAILIQL

Company Brief

Website

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