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Deep Reinforcement Learning Jobs (NOW HIRING)

Apptronik

Senior Reinforcement Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

JOB SUMMARY The Senior Reinforcement Learning Engineer is a key, hands-on role focused on achieving ... This engineer will leverage their deep expertise in RL to solve critical locomotion and ...

Traversal

AI Researcher

Manhattan, NY · On-site

As an AI Researcher, you will train intelligent AI agents for root cause analysis in high-pressure environments, leading research in areas like LLM fine-tuning and deep reinforcement learning.

Apptronik

Reinforcement Learning Engineer

Austin, TX · On-site

A strong theoretical understanding of modern reinforcement learning, including deep expertise in areas like imitation learning, model-based RL, and sim-to-real transfer techniques. * A strong ...

Bostondynamics

Staff, ML Research Scientist

Waltham, MA

$154K - $192K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

Experience in the full modeling cycle from research to deployment of modern Deep Learning architectures such as Transformers, VLMs/VLAs, and Deep Reinforcement Learning. * Knowledge of ...

Boston Dynamics

Staff, ML Research Scientist

Waltham, MA · On-site

$154K - $192K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

Experience in the full modeling cycle from research to deployment of modern Deep Learning architectures such as Transformers, VLMs/VLAs, and Deep Reinforcement Learning. * Knowledge of ...

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How much do deep reinforcement learning jobs pay per year?

As of Aug 18, 2026, the average yearly pay for deep reinforcement learning in the United States is $58,347.00, according to ZipRecruiter salary data. Most workers in this role earn between $50,500.00 and $68,000.00 per year, depending on experience, location, and employer.

What is deep reinforcement learning?

A Deep Reinforcement Learning (DRL) job involves researching, developing, and applying AI models that use reinforcement learning techniques combined with deep learning. Professionals in this role design algorithms that enable agents to learn optimal decision-making policies through trial and error. Common applications include robotics, game AI, autonomous systems, and financial modeling. This job typically requires expertise in machine learning, neural networks, and programming languages like Python, along with frameworks such as TensorFlow or PyTorch.

What does a typical day look like for someone working in deep reinforcement learning?

A typical day for a Deep Reinforcement Learning professional involves designing algorithms, running experiments, analyzing results, and optimizing models to improve performance. You may collaborate regularly with data scientists, software engineers, and domain experts to integrate RL solutions into larger systems or products. Tasks often include reading the latest research, contributing to code reviews, and documenting findings while troubleshooting technical challenges. This dynamic environment encourages continuous learning and teamwork, ensuring you stay at the forefront of AI innovation.

What are the key skills and qualifications needed to thrive in deep reinforcement learning?

To thrive in Deep Reinforcement Learning, you need expertise in machine learning, programming (Python, TensorFlow, or PyTorch), and applied mathematics, often supported by an advanced degree in computer science or a related field. Familiarity with version control systems, cloud computing platforms, and relevant certifications in AI or data science are valuable assets. Strong problem-solving abilities, collaboration, and effective communication are important soft skills in this position. These skills are essential for developing, implementing, and iterating cutting-edge algorithms that solve complex real-world problems in dynamic environments.

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Deep Reinforcement Learning jobs near you
Infographic showing various Deep Reinforcement Learning job openings in the United States as of August 2026, with employment types broken down into 73% Full Time, 16% Part Time, and 11% Contract. Highlights an 84% In-person, and 16% Remote job distribution, with an average salary of $58,347 per year, or $28.1 per hour.

Senior Reinforcement Learning Engineer

Apptronik

Sunnyvale, CA • On-site

$122K - $168K/yr

Full-time

Posted 5 days ago

Job description

JOB SUMMARY

The Senior Reinforcement Learning  Engineer is a key, hands-on role focused on achieving state-of-the-art performance on our humanoid robots. This engineer will leverage their deep expertise in RL to solve critical locomotion and manipulation challenges and deliver breakthrough results on physical hardware. The primary focus of this role is to rapidly implement, iterate, and deploy advanced learning algorithms to push the boundaries of what our robots can do. As a senior member of the team, this individual will also be responsible for mentoring junior engineers, elevating the team's overall technical capabilities through their guidance and expertise.

ESSENTIAL DUTIES AND RESPONSIBILITIES or KEY ACCOUNTABILITIES

  • Implement and deploy state-of-the-art RL algorithms to achieve ambitious, world-class performance on dynamic locomotion and manipulation tasks with physical hardware.
  • Drive the entire development cycle, from prototyping in simulation to robustly transferring and fine-tuning policies on the robot.
  • Optimize and scale the RL training pipeline for faster iteration, contributing to core infrastructure for high-throughput simulation and distributed training.
  • Mentor junior engineers by providing technical guidance, conducting insightful code reviews, and sharing best practices in reinforcement learning and software development.
  • Collaborate closely with the robotics and hardware teams to diagnose system-level issues and co-develop solutions that enable more complex learned behaviors.
  • Analyze and present hardware results to guide future technical directions and demonstrate progress on key company objectives.
  • Develop and refine motion retargeting pipelines to translate human demonstration data (mocap, teleoperation) into robust reference trajectories for reinforcement learning.

SKILLS AND REQUIREMENTS

  • Deep, hands-on expertise (5+ years) with common RL frameworks (e.g., PyTorch, JAX) and high-fidelity physics simulators (e.g., MuJoCo, IsaacGym)
  • Mastery of Python for rapid prototyping and training, alongside strong proficiency in C++ for developing performant, deployable code.
  • Experience building or utilizing large-scale, distributed training pipelines and a strong intuition for their optimization.
  • A strong theoretical understanding of modern reinforcement learning, including deep expertise in areas like imitation learning, model-based RL, and sim-to-real transfer techniques.
  • A strong intuition for robot dynamics and controls theory, with the ability to apply these principles to guide and constrain learning-based approaches.
  • A results-oriented mindset with a passion for seeing complex algorithms work on real-world hardware.

EDUCATION and/or EXPERIENCE

  • A PhD or MS in Computer Science, Robotics, or a related field, with 2+ years industry experience strongly preferred.
  • A proven track record of successfully deploying learning-based policies on physical robotic systems, especially legged robots or manipulators.
  • Demonstrated experience mentoring or providing technical guidance to other engineers in a team environment.
  • A strong publication record in relevant conferences or journals (e.g., CoRL, RSS, ICRA) is a significant plus.

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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