Senior Reinforcement Learning Engineer

Apptronik
Sunnyvale, Austin
Workplace: OnsiteFull timeFunction: Education & TrainingExperience: 5+ yearsEducation: mastersSkills: ["Mentoring","Code review","Collaboration","Results-oriented mindset","Technical guidance"]

Design and deploy state-of-the-art reinforcement learning algorithms for Apollo humanoid robots, driving performance gains in dynamic locomotion and manipulation. Own the full cycle from simulation prototyping to sim-to-real policy transfer and fine-tuning. Scale distributed RL training pipelines for rapid iteration, mentor junior engineers through guidance and code reviews, and collaborate with robotics and hardware teams to diagnose issues and advance motion retargeting from human demonstration data.

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FursaFursa
Apptronik
Apptronik
1 day ago

Senior Reinforcement Learning Engineer

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

Source: Company careers pageValidated by: Fursa AI
Last checked: 6 hours agoStatus: Live

Job Summary

Design and deploy state-of-the-art reinforcement learning algorithms for Apollo humanoid robots, driving performance gains in dynamic locomotion and manipulation. Own the full cycle from simulation prototyping to sim-to-real policy transfer and fine-tuning. Scale distributed RL training pipelines for rapid iteration, mentor junior engineers through guidance and code reviews, and collaborate with robotics and hardware teams to diagnose issues and advance motion retargeting from human demonstration data.
Location: Sunnyvale, 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 with physical hardware.
  • •Lead prototyping in simulation through robust policy transfer and fine-tuning on robots.
  • •Optimize and scale RL training pipelines, contributing to infrastructure for high-throughput simulation and distributed training.
  • •Mentor junior engineers via technical guidance, insightful code reviews, and sharing reinforcement learning and software best practices.
  • •Collaborate with robotics and hardware teams to diagnose system-level issues and develop solutions; analyze hardware results to guide future directions.

Key Requirements

  • •5+ years of hands-on reinforcement learning experience using frameworks such as PyTorch and JAX and physics simulators such as MuJoCo and IsaacGym.
  • •Proficiency in Python for rapid prototyping and training, with strong C++ skills for performant, deployable code.
  • •Experience building or using large-scale, distributed training pipelines and optimizing them.
  • •Strong theoretical understanding of modern RL, including imitation learning, model-based RL, and sim-to-real transfer.
  • •Track record deploying learning-based policies on real robotic systems, especially legged robots or manipulators, with experience mentoring engineers.
Experience:5+ yearsRoboticsReinforcement learningSim-to-realDistributed training
Education:Master's in Computer Science, Robotics, or a related field
Skills:MentoringCode reviewCollaborationResults-oriented mindsetTechnical guidance
Languages:English
Tech Stack:PythonPyTorchJAXC++MuJoCoIsaacGymDistributed trainingSimulationSim-to-realMocapTeleoperationReinforcement learningImitation learningModel-based RL

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