Reinforcement Learning Engineer

Apptronik
Austin
Workplace: OnsiteFull timeFunction: Education & TrainingExperience: 3+ yearsEducation: phdSkills: ["Results-oriented","Collaboration","Rigorous code reviews","Technical guidance","Analyzing results"]

Build and deploy state-of-the-art reinforcement learning algorithms for dynamic locomotion and whole-body locomanipulation on humanoid robots. Lead work from simulation prototyping through sim-to-real transfer and fine-tuning, while optimizing scalable distributed training pipelines. Develop motion retargeting pipelines from mocap/teleoperation data, collaborate with robotics and hardware teams to resolve system issues, and analyze hardware results to guide technical direction.

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FursaFursa
Apptronik
Apptronik
3 days ago

Reinforcement Learning Engineer

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Source: Company careers pageValidated by: Fursa AI
Last checked: 3 hours agoStatus: Live

Job Summary

Build and deploy state-of-the-art reinforcement learning algorithms for dynamic locomotion and whole-body locomanipulation on humanoid robots. Lead work from simulation prototyping through sim-to-real transfer and fine-tuning, while optimizing scalable distributed training pipelines. Develop motion retargeting pipelines from mocap/teleoperation data, collaborate with robotics and hardware teams to resolve system issues, and analyze hardware results to guide technical direction.
Location: Austin
Workplace: Onsite
Employment Type: Full time
Job Function: Education & Training
Seniority: Mid level

Key Responsibilities

  • •Implement and deploy state-of-the-art RL algorithms for dynamic locomotion and manipulation tasks with physical hardware.
  • •Drive the development cycle from simulation prototyping through transferring and fine-tuning policies on the robot.
  • •Optimize and scale the RL training pipeline for faster iteration, including high-throughput simulation and distributed training infrastructure.
  • •Develop and refine motion retargeting pipelines that translate human demonstration data (mocap, teleoperation) into reinforcement learning reference trajectories.
  • •Collaborate with robotics and hardware teams to diagnose system-level issues and co-develop solutions for more complex learned behaviors.

Key Requirements

  • •Hands-on expertise (3+ years) with reinforcement learning frameworks such as PyTorch and JAX and high-fidelity physics simulators like MuJoCo or IsaacGym.
  • •Mastery of Python for rapid prototyping and training, plus strong proficiency in C++ for performant, deployable code.
  • •Experience building or using large-scale, distributed training pipelines and knowledge of optimizing them.
  • •Deep theoretical understanding of modern reinforcement learning, including imitation learning, model-based RL, and sim-to-real transfer.
  • •Experience mentoring or providing technical guidance and a proven record deploying learning-based policies on physical robotic systems.
Experience:3+ yearsRoboticsReinforcement learningSim-to-real
Education:PhD / Doctorate in Computer Science, Robotics, or a related field
Skills:Results-orientedCollaborationRigorous code reviewsTechnical guidanceAnalyzing results
Languages:English
Tech Stack:PyTorchJAXPythonC++MuJoCoIsaacGymDistributed trainingSimulationReinforcement learningImitation learningModel-based RLSim-to-real transfer

Company Brief

Apptronik
Develops advanced general-purpose robots and robotic limbs for industrial and commercial applications, combining hardware, actuators, and control software to enable versatile, human-scale robot solutions.
Industry: Robotics
Company Size: Medium (51 to 250 employees)
Growth: Growth Stage Startup
Funding: Series A
Headquarters: Austin, United States
Founded: 2018
WebsiteLinkedIn