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Openai Reinforcement Learning Jobs (NOW HIRING)

OpenAI

Researcher, Synthetic RL

San Francisco, CA · On-site

$295K - $445K/yr

About the Team The Synthetic RL team develops reinforcement learning methods that leverage ... Are motivated by seeing research ideas influence real-world AI systems About OpenAI OpenAI is an AI ...

Centific

Applied Reinforcement Learning Engineer

Applied Reinforcement Learning Engineer Location: Palo Alto, CA or Seattle, WA (Hybrid/Remote ... • Gymnasium/OpenAI Gym: Custom environments, observation/action spaces, wrapper patterns • ...

OpenAI

Researcher, Alignment Science

San Francisco, CA · On-site

$250K - $445K/yr

The team uses a mix of training and evaluation methods, with a focus on reinforcement learning. We ... About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general ...

Statheros

Develop and train reinforcement learning models for real-world applications, focusing on efficiency ... Familiarity with OpenAI Gym, RLlib, or other RL development environments * Knowledge of parallel ...

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Openai Reinforcement Learning information

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How much do openai reinforcement learning jobs pay per year?

As of Jun 12, 2026, the average yearly pay for openai 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.

Is ML a high paying job?

Machine Learning (ML) roles, including positions like OpenAI Reinforcement Learning engineers, are generally well-paid due to the specialized skills required, such as programming, data analysis, and knowledge of algorithms. Salaries tend to be higher than average in tech hubs and often include benefits like stock options and bonuses, reflecting the demand for expertise in AI and ML development.

What is the difference between Openai Reinforcement Learning vs Machine Learning Engineer?

AspectOpenai Reinforcement LearningMachine Learning Engineer
Required CredentialsKnowledge of reinforcement learning algorithms, programming skills, possibly advanced degrees in AI or CSStrong programming skills, proficiency in ML frameworks, degrees in CS, data science, or related fields
Work EnvironmentResearch labs, AI development teams, experimental projectsTech companies, startups, data-driven organizations, software development teams
Industry UsageAI research, autonomous systems, game AI, roboticsProduct development, predictive modeling, data analysis, deployment of ML models

Openai Reinforcement Learning focuses on developing algorithms that enable agents to learn through trial and error in dynamic environments, often in research settings. Machine Learning Engineers implement and optimize ML models for practical applications across various industries. While both roles require programming skills and knowledge of AI, reinforcement learning specialists concentrate on experimental algorithms, whereas ML engineers focus on deploying scalable solutions.

Is it hard to get hired at OpenAI?

Getting hired for roles related to OpenAI reinforcement learning typically requires a strong background in machine learning, deep learning, and programming skills in Python. Candidates often need relevant research experience, a solid understanding of reinforcement learning algorithms, and a track record of contributions to AI projects or publications. The hiring process is competitive and may include technical interviews and assessments.

What are the key skills and qualifications needed to thrive as an OpenAI Reinforcement Learning Engineer, and why are they important?

To excel as an OpenAI Reinforcement Learning Engineer, you need a solid background in computer science, machine learning, and mathematics, often supported by advanced degrees (MS/PhD) in related fields. Experience with deep learning frameworks like TensorFlow or PyTorch, familiarity with RL-specific libraries (such as OpenAI Gym), and strong programming skills in Python are critical. Problem-solving ability, collaboration, and clear communication distinguish outstanding candidates in this role. These competencies are vital for designing, implementing, and optimizing RL algorithms that advance state-of-the-art AI systems.

Does OpenAI do reinforcement learning?

OpenAI actively researches and applies reinforcement learning techniques to develop advanced AI models, including training agents to improve performance through trial and error. Reinforcement learning is a core component of some OpenAI projects, and expertise in this area is valuable for roles involving AI development and machine learning. Knowledge of algorithms like Q-learning and policy optimization is often required.

What is OpenAI Reinforcement Learning?

OpenAI Reinforcement Learning refers to the use of reinforcement learning (RL) techniques, often developed or researched by OpenAI, to train artificial intelligence agents. In reinforcement learning, agents learn to make decisions by interacting with an environment to maximize accumulated rewards. OpenAI has pioneered several advancements in RL, such as training agents to play games, control robots, and solve complex sequential problems. These approaches have led to breakthroughs like OpenAI Five for Dota 2 and improvements in general AI capabilities. RL at OpenAI often involves large-scale simulations, deep learning, and innovative exploration strategies.

How does an OpenAI Reinforcement Learning Engineer typically collaborate with researchers and software engineers on projects?

As an OpenAI Reinforcement Learning Engineer, you will frequently work in cross-functional teams that include research scientists, software engineers, and sometimes product managers. Collaboration often involves jointly designing experiments, sharing insights from model performance, and integrating new algorithms into production environments. Clear communication and documentation are vital, as your work may directly inform ongoing research and product development. You'll participate in regular meetings, code reviews, and brainstorming sessions to ensure alignment and drive innovation.

Which 3 jobs will survive AI?

In the field of OpenAI Reinforcement Learning, jobs such as AI research scientists, machine learning engineers, and data scientists are likely to persist as they require advanced understanding of complex algorithms, programming skills, and domain expertise. These roles involve designing, developing, and refining AI models, which are less susceptible to automation due to their specialized knowledge and problem-solving requirements.
Infographic showing various Openai Reinforcement Learning job openings in the United States as of June 2026, with employment types broken down into 9% Locum Tenens, 76% Full Time, 2% Contract, 11% Nights, and 2% Summer. Highlights an 92% Physical, 2% Hybrid, and 6% Remote job distribution, with an average salary of $58,347 per year, or $28.1 per hour.
Helix AI Engineer, Reinforcement Learning

Helix AI Engineer, Reinforcement Learning

Figure

San Jose, CA • On-site, Remote

Other

Posted 4 days ago

Job description

Figure is an AI robotics company developing autonomous general-purpose humanoid robots. Our goal is to build embodied AI systems that can perceive, reason, and act in the real world. Figure is headquartered in San Jose, CA, and this role requires 5 days/week in-office collaboration.

Our Helix team is responsible for developing the core AI systems that power humanoid autonomy. We are looking for a Helix AI Engineer, Reinforcement Learning to develop learning systems that enable robots to acquire skills through interaction, feedback, and experience.

This role focuses on applying and advancing reinforcement learning across simulation and real-world environments-improving policy performance, robustness, and long-horizon decision-making in embodied systems.

Responsibilities
  • Design and implement reinforcement learning algorithms for embodied agents operating in real-world and simulated environments
  • Train policies that learn from interaction, feedback, and large-scale experience across diverse tasks
  • Develop reward modeling, credit assignment, and exploration strategies for complex, long-horizon behaviors
  • Improve policy robustness to real-world challenges such as noise, partial observability, and environment variability
  • Work across online and offline RL settings, including learning from large-scale logged robot data
  • Collaborate closely with pretraining, video, generative, agent, and robot learning teams to integrate RL into the full autonomy stack
  • Build scalable training systems for RL, including distributed rollouts, simulation infrastructure, and experiment management
  • Design evaluation frameworks to measure policy performance, stability, and generalization
Requirements
  • Experience developing and applying reinforcement learning algorithms in complex environments
  • Strong understanding of RL fundamentals (e.g., policy optimization, value methods, model-based RL)
  • Experience training policies in simulation and/or real-world systems
  • Proficiency in Python and deep learning frameworks such as PyTorch
  • Experience with large-scale experimentation and distributed training systems
  • Strong experimental rigor and ability to diagnose and improve learning systems
  • Solid software engineering skills and ability to build scalable, reliable systems
  • Ability to operate independently and drive ambiguous, high-impact technical problems
Bonus Qualifications
  • Experience applying RL to robotics, control systems, or embodied AI
  • Experience with large-scale RL infrastructure (distributed rollouts, simulation at scale)
  • Background in offline RL, imitation learning, or hybrid learning approaches
  • Experience with reward modeling or human-in-the-loop learning
  • Experience at leading AI labs such as OpenAI, Google DeepMind, Anthropic, or xAI
  • Familiarity with robotics systems, simulation environments, or real-world deployment constraints
  • Publication record in reinforcement learning, machine learning, or robotics

The pay offered for this position may vary based on several individual factors, including job-related knowledge, skills, and experience. The total compensation package may also include additional components/benefits depending on the specific role. This information will be shared if an employment offer is extended. 

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