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Postdoctoral In Reinforcement Learning Jobs (NOW HIRING)

Skild AI

Machine Learning Engineer

San Mateo, CA · On-site

Stay up-to-date with the latest research and advancements in reinforcement learning. Preferred Qualifications * BS, MS or higher degree in Computer Science, Robotics, Engineering or a related field ...

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 ...

Dexmate

Reinforcement learning engineer

Santa Clara, CA · On-site

Responsibilities : • Design and implement reinforcement learning algorithms for various robotics tasks • Develop and optimize RL training pipelines in both simulation and real-world environments ...

Virginia Tech

Postdoctoral Associate

Blacksburg, VA

Postdoctoral Associate Apply now Back to search results Job no: 534769 Work type: Research Faculty ... Expertise in reinforcement learning and/or tensor networks are preferred. Other responsibilities ...

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How much do postdoctoral in reinforcement learning jobs pay per year?

As of Jul 12, 2026, the average yearly pay for postdoctoral in reinforcement learning in the United States is $59,022.00, according to ZipRecruiter salary data. Most workers in this role earn between $49,000.00 and $66,500.00 per year, depending on experience, location, and employer.

What is the difference between Postdoctoral In Reinforcement Learning vs Postdoctoral In Machine Learning?

AspectPostdoctoral In Reinforcement LearningPostdoctoral In Machine Learning
Required CredentialsPhD in Computer Science, AI, or related field; strong programming skills; research experience in reinforcement learningPhD in Computer Science, AI, or related field; strong programming skills; research experience in machine learning
Work EnvironmentAcademic labs, research institutions, industry R&D teams focused on reinforcement learning applicationsAcademic labs, research institutions, industry R&D teams working on various machine learning techniques
Industry UsagePrimarily in AI research, robotics, gaming, and autonomous systemsBroader applications including data analysis, predictive modeling, and AI research

Postdoctoral In Reinforcement Learning specializes in research related to decision-making algorithms and autonomous systems, whereas Postdoctoral In Machine Learning covers a wider range of AI techniques. Both roles require similar credentials but differ in focus and application areas.

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

To thrive as a Postdoctoral Researcher in Reinforcement Learning, you need a PhD in computer science or a related field, with deep expertise in machine learning, statistics, and algorithm development. Proficiency in programming languages such as Python, experience with deep learning frameworks (e.g., TensorFlow or PyTorch), and familiarity with reinforcement learning libraries are typically required. Strong analytical thinking, problem-solving ability, collaboration, and scientific communication skills help you excel in research teams and publish impactful work. These competencies are vital to advancing state-of-the-art research, developing novel algorithms, and contributing to the academic and industrial progress in AI.

What are some common challenges faced by postdoctoral researchers in reinforcement learning, and how can they be addressed?

Postdoctoral researchers in reinforcement learning often face challenges such as balancing independent research projects with collaborative work, staying up-to-date with rapidly evolving literature, and managing the pressure to publish in top conferences. Effective time management, regular engagement with the research community through seminars and workshops, and seeking mentorship from senior colleagues can help address these challenges. Additionally, collaborating with interdisciplinary teams can offer fresh perspectives and support, making it easier to navigate complex research problems.

What is a Postdoctoral Researcher in Reinforcement Learning?

A Postdoctoral Researcher in Reinforcement Learning is an individual who has completed a PhD and conducts advanced research in the field of reinforcement learning, a branch of artificial intelligence focused on how agents take actions in environments to maximize rewards. These researchers often work in academic, industrial, or governmental research settings, collaborating on projects that advance the theoretical foundations or practical applications of reinforcement learning. Their responsibilities may include designing experiments, developing algorithms, publishing papers, and mentoring graduate students.
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Infographic showing various Postdoctoral In Reinforcement Learning job openings in the United States as of July 2026, with employment types broken down into 1% As Needed, 73% Full Time, 21% Part Time, 1% Temporary, and 4% Contract. Highlights an 92% Physical, 1% Hybrid, and 7% Remote job distribution, with an average salary of $59,022 per year, or $28.4 per hour.
Research Scientist, Reinforcement Learning - Atlas

Research Scientist, Reinforcement Learning - Atlas

Boston Dynamics

Waltham, MA • On-site

Full-time

Re-posted 15 days ago

Job description

Job Summary:
Boston Dynamics is pushing the boundaries of what advanced humanoid robots can do in the real world, and they are seeking a curious, driven Research Scientist to develop cutting-edge reinforcement learning solutions. The role involves designing, training, and deploying RL policies for complex tasks in unstructured environments while collaborating with a world-class team of roboticists.
Responsibilities:
• Design, implement, and train reinforcement learning algorithms for challenging whole-body mobile manipulation and bimanual manipulation tasks.
• Develop high-quality Python and C++ code that is tested, documented, and production-ready.
• Build and leverage high-fidelity simulation environments (e.g., Isaac Sim, MuJoCo) to validate RL policies before deploying on hardware.
• Integrate learned policies with Atlas’s control and software stack through close collaboration with controls and platform teams.
• Deploy, debug, and iterate policies directly on real Atlas hardware through hands-on experimentation.
• Participate in design reviews, experimental planning, and team-wide research direction.
Qualifications:
Required:
• MS or PhD in Computer Science, Machine Learning, Robotics, or a related field.
• Strong experience training and deploying RL policies for complex behaviors in robots or simulated agents.
• Proficiency with modern ML frameworks (e.g., PyTorch, TensorFlow, RLlib).
• Strong foundations in algorithms, debugging, performance optimization, and robotics fundamentals (kinematics, dynamics).
• Excellent Python and C++ programming skills and experience contributing to production-scale software.
Preferred:
• PhD or equivalent research experience in reinforcement learning or robotic manipulation.
• Experience deploying RL policies on physical robots.
• Experience developing locomotion, bimanual manipulation, or whole-body control behaviors.
• Contributions to large software projects or open-source ML/robotics frameworks.
• Publications in top-tier robotics or ML conferences (e.g., CoRL, RSS, ICRA, NeurIPS).
Company:
Boston Dynamics is an engineering company that specializes in building dynamic robots and software for human simulation. It is a sub-organization of Hyundai Motor Company. Founded in 1992, the company is headquartered in Waltham, USA, with a team of 501-1000 employees. The company is currently Late Stage.

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