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Ai Reinforcement Learning Jobs (NOW HIRING)

Dexmate

Reinforcement learning engineer

Santa Clara, CA · On-site

Dexmate is building the foundation for physical AI -- a unified platform that combines high-quality robotic hardware with a universal Physical AI OS. They are seeking Reinforcement Learning experts ...

Jobtailor

Principal AI/ML Engineer

Manhattan, NY · On-site

$180 - $260/hr

Research and apply state-of-the-art AI methodologies, including LLMs, transformers, and reinforcement learning * Lead AI strategy by identifying opportunities for innovation and model optimization

New

Apptronik

Senior Reinforcement Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

Apptronik is a human-centered robotics company developing AI-powered robots to support humanity in every facet of life. The Senior Reinforcement Learning Engineer will leverage their expertise in ...

Apptronik

Senior Reinforcement Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

Apptronik is a human-centered robotics company developing AI-powered robots to support humanity in every facet of life. The Senior Reinforcement Learning Engineer will focus on achieving ...

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How much do ai reinforcement learning jobs pay per hour?

As of Aug 17, 2026, the average hourly pay for ai reinforcement learning in the United States is $40.70, according to ZipRecruiter salary data. Most workers in this role earn between $29.57 and $52.88 per hour, depending on experience, location, and employer.

What is AI reinforcement learning?

AI reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or penalties based on its actions, which it uses to improve its future performance. Reinforcement learning is widely used in applications such as robotics, game playing, recommendation systems, and autonomous vehicles. Unlike supervised learning, RL doesn't require labeled input/output pairs and learns through trial and error.

What are the key skills and qualifications needed to thrive as an AI reinforcement learning specialist?

To thrive as an AI Reinforcement Learning Specialist, you need strong expertise in machine learning, deep learning, and mathematics, usually backed by a degree in computer science, engineering, or a related field. Familiarity with programming languages like Python, frameworks such as TensorFlow or PyTorch, and experience with RL-specific libraries like OpenAI Gym are typically required. Analytical thinking, problem-solving abilities, and effective collaboration are essential soft skills for excelling in this role. These skills and qualifications are crucial for developing, optimizing, and deploying RL algorithms that solve complex, real-world problems.

What are some common challenges faced by AI reinforcement learning specialists when deploying models in real-world applications?

AI Reinforcement Learning (RL) specialists often encounter challenges such as ensuring the reliability and safety of RL agents outside of controlled environments. Real-world data can be noisy and unpredictable, making it difficult for models trained in simulations to generalize. Additionally, RL algorithms typically require significant computational resources and time for training, which can be a constraint in fast-paced projects. Collaboration with domain experts and software engineers is essential to adapt algorithms to production systems and continuously monitor performance for unexpected behaviors.

What is the difference between Ai Reinforcement Learning vs Data Scientist?

AspectAi Reinforcement LearningData Scientist
Required CredentialsDegree in Computer Science, AI, or related fields; knowledge of algorithmsDegree in Statistics, Data Science, or related fields; programming skills
Work EnvironmentResearch labs, AI development teams, tech companiesBusiness analytics, data analysis teams, consulting firms
Industry UsageAI product development, autonomous systems, roboticsBusiness insights, predictive modeling, data analysis
Common Search/ComparisonYesYes

Ai Reinforcement Learning focuses on developing algorithms that enable machines to learn through trial and error to make decisions. Data Scientists analyze data to extract insights and build predictive models. While both roles require programming skills and a background in data or algorithms, reinforcement learning specialists primarily work on AI systems that learn from interactions, whereas Data Scientists focus on interpreting data to inform business decisions.

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Ai Reinforcement Learning jobs near you
Infographic showing various Ai Reinforcement Learning job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 23% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $84,648 per year, or $40.7 per hour.

Helix AI Engineer, Reinforcement Learning

Figure

San Jose, CA • On-site

Full-time

Re-posted 10 days ago

Job description

Job Summary:
Figure is an AI robotics company developing autonomous general-purpose humanoid robots. They are seeking a Helix AI Engineer, Reinforcement Learning to develop learning systems that enable robots to acquire skills through interaction, feedback, and experience.
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
Qualifications:
Required:
• 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
Preferred:
• 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
Company:
Figure is an AI robotics company that develops autonomous general-purpose humanoid robots. Founded in 2022, the company is headquartered in San Jose, USA, with a team of 201-500 employees. The company is currently Growth Stage.

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