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Reinforcement Learning Game Jobs (NOW HIRING)

Apptronik

Software Engineer - Human Motion Data

Austin, TX

$113.50K - $136.30K/yr

... and our reinforcement learning pipelines. This role is dedicated to architecting robust motion data pipelines-integrating diverse sources like mocap, game engines (like Unreal or Unity ...

Apptronik

Software Engineer - Human Motion Data

Austin, TX · On-site

$113.50K - $136.30K/yr

... and our reinforcement learning pipelines. This role is dedicated to architecting robust motion data pipelines-integrating diverse sources like mocap, game engines (like Unreal or Unity ...

Medal

World Model / Action Policy Researcher

New York, NY · On-site

$350K - $450K/yr

What We're Looking For * 5+ years of experience in deep learning research or reinforcement learning ... Gaming & simulation passion: Interest in interactive environments, physics-based simulations, or ...

Quantiply Corporation

Data Scientist

San Jose, CA · On-site

... game theory or other technical field. * Strong knowledge of mathematics. * Strong knowledge of ... Strong knowledge of deep learning. * Strong knowledge of reinforcement learning. * Strong ...

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How much do reinforcement learning game jobs pay per hour?

As of May 31, 2026, the average hourly pay for reinforcement learning game in the United States is $20.94, according to ZipRecruiter salary data. Most workers in this role earn between $14.18 and $23.80 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Reinforcement Learning Engineer in the gaming industry, and why are they important?

To thrive as a Reinforcement Learning Engineer in game development, you need a strong background in machine learning, algorithms, and programming (typically Python), often supported by a degree in computer science or a related field. Familiarity with frameworks like TensorFlow, PyTorch, and RL-specific libraries (such as OpenAI Gym or Unity ML-Agents), as well as experience with simulation environments, is typically required. Critical thinking, creativity, and effective communication help you design innovative AI solutions and collaborate with interdisciplinary teams. These skills are crucial for developing intelligent game agents that enhance player experience and drive technological advancement in interactive entertainment.

How does a Reinforcement Learning Game Engineer typically collaborate with game designers and data scientists during development?

As a Reinforcement Learning Game Engineer, you will frequently collaborate with game designers to integrate RL agents in ways that enhance gameplay and balance. Close coordination with data scientists is also common, as they help analyze agent behaviors and performance data to refine training environments and reward structures. Regular cross-functional meetings and iterative testing sessions are standard, ensuring that RL-driven features both align with the game's vision and deliver measurable improvements. This collaborative environment fosters innovation and provides valuable insights into both AI and game design best practices.

What is a Reinforcement Learning Game?

A Reinforcement Learning (RL) Game is a simulation or environment designed for testing and training artificial intelligence (AI) agents using reinforcement learning techniques. In these games, an agent interacts with the environment by taking actions and receiving rewards based on its performance, allowing it to learn optimal strategies over time. RL games are widely used in research to benchmark algorithms and in industry to develop intelligent behaviors for robots, automated systems, or video game characters. Popular RL games include OpenAI Gym environments, Atari games, and custom simulations built for specific tasks.
Infographic showing various Reinforcement Learning Game job openings in the United States as of May 2026, with employment types broken down into 55% Full Time, 21% Part Time, 7% Temporary, 16% Contract, and 1% Summer. Highlights an 37% Physical, 37% Hybrid, and 26% Remote job distribution, with an average salary of $43,561 per year, or $20.9 per hour.
Software Engineer - Human Motion Data

Software Engineer - Human Motion Data

Apptronik

Austin, TX

$113.50K - $136.30K/yr

Other

Posted 19 days ago

Job description

JOB SUMMARY

As a Software Engineer- Human Motion Data, you will leverage your background in robotics to build the crucial link between human-data and our reinforcement learning pipelines. This role is dedicated to architecting robust motion data pipelines-integrating diverse sources like mocap, game engines (like Unreal or Unity), teleoperation, and generative AI motion models to generate thousands of rich, physically accurate human motion trajectories. You will apply your deep expertise in kinematics and rigid body dynamics to translate raw human movement into actionable, dynamically feasible data for whole-body reinforcement learning. As a core member of the Motion Control and Planning team, you will work closely with Controls stakeholders to play a key role in maintaining a high-velocity, ego-free engineering culture while ensuring our humanoid robots move with unprecedented fluidity.

ESSENTIAL DUTIES AND RESPONSIBILITIES or KEY ACCOUNTABILITIES

  • Design, build, and maintain end-to-end motion data pipelines, integrating diverse sources such as motion capture (mocap), teleoperation, and synthetic generation using diffusion models, animation and gaming engines, to support humanoid robot development.
  • Implement and optimize kinematic and dynamic retargeting pipelines to accurately map human demonstrations onto the robot's specific physical constraints, mass distributions, and joint limits.
  • Develop tools and scripts to process and clean raw human demonstration data, and apply state-of-the-art retargeting libraries (e.g., GMR, Omni-retarget) to synthesize and filter new behaviors.
  • Leverage game engines (Unreal Engine or Unity) and physics simulators to build simulated environments for procedural motion generation and data augmentation.
  • Generate high-volume, high-quality trajectory datasets required for training whole-body reinforcement learning policies.
  • Write robust, automated pipelines to streamline data flow between human demonstration sources, generative motion models, game engines, and the RL training infrastructure.
  • Collaborate closely with the Reinforcement Learning and Controls teams to iterate on data requirements, understand failure modes, and ensure the generated trajectories are physically viable on hardware.

SKILLS AND REQUIREMENTS

  • Strong theoretical and practical understanding of robot kinematics (FK/IK), coordinate transformations, and rigid body dynamics.
  • Experience building or maintaining pipelines for spatial data, including motion capture, teleoperation tracking, or AI-driven motion generation.
  • Expertise in Python for scripting, data processing, and pipeline automation.
  • A results-oriented mindset with an eagerness to bridge the gap between human motion data and real-world robotic control algorithms.
  • High adaptability and a willingness to explore new tools, moving seamlessly across different layers of the robotics software stack as project needs evolve.NICE TO HAVE (BONUS QUALIFICATIONS)
    • Hands-on experience with state-of-the-art motion generation models and open-source retargeting libraries (e.g., GMR, Omni-retarget).
    • Proficiency in C++ is highly valued to help integrate with our broader robotics software stack.
    • Familiarity and hands-on experience utilizing 3D game engines (Unreal Engine or Unity) or advanced physics simulators for robotics data generation or simulation.
    • Hardware (HW) experience, particularly working directly with physical robotic platforms.
    • A strong portfolio showcasing relevant robotics, sim-to-real, or motion generation projects.

EDUCATION and/or EXPERIENCE

  • A BS or MS degree in Robotics, Mechanical Engineering, Computer Science, or a related highly technical field.
  • 2+ years of industry or applied research experience in robotics, motion planning, sim-to-real pipelines, or technical animation data generation.
  • A proven track record of processing large amounts of spatial or motion data to drive robotic or simulated systems.

PHYSICAL REQUIREMENTS

  • Prolonged periods of sitting at a desk and working on a computer
  • Must be able to lift 15 pounds at times
  • Vision to read printed materials and a computer screen
  • Hearing and speech to communicate

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