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Reinforcement Learning Optimization Jobs (NOW HIRING)

Apptronik

Senior Reinforcement Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

Experience building or utilizing large-scale, distributed training pipelines and a strong intuition for their optimization. * A strong theoretical understanding of modern reinforcement learning ...

Centific

Applied Reinforcement Learning Engineer

Applied Reinforcement Learning Engineer Location: Palo Alto, CA or Seattle, WA (Hybrid/Remote ... DPO, IPO, KTO, offline preference optimization • Group-based methods: GRPO, RLOO, sample ...

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Reinforcement Learning Optimization information

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$11K

$83.9K

$140K

How much do reinforcement learning optimization jobs pay per year?

As of Sep 10, 2026, the average yearly pay for reinforcement learning optimization in the United States is $83,885.00, according to ZipRecruiter salary data. Most workers in this role earn between $72,000.00 and $139,000.00 per year, depending on experience, location, and employer.

What is reinforcement learning optimization?

Reinforcement Learning Optimization is a process in machine learning where agents learn to make decisions by interacting with an environment to achieve a specific goal. Through trial and error, the agent receives feedback in the form of rewards or penalties, which it uses to refine its actions over time. This optimization technique is widely used in robotics, gaming, and autonomous systems to develop intelligent behaviors. The core idea is to maximize cumulative rewards by finding the best sequence of decisions. Reinforcement Learning Optimization combines elements of computer science, mathematics, and statistics to solve complex real-world problems.

What are the key skills and qualifications needed to thrive as a reinforcement learning optimization specialist?

To thrive in Reinforcement Learning Optimization, a strong background in mathematics, probability theory, machine learning algorithms, and programming (often Python) is essential, typically supported by an advanced degree in computer science or a related field. Familiarity with deep learning frameworks (such as TensorFlow or PyTorch), experience with RL libraries (like OpenAI Gym), and knowledge of optimization techniques are highly valued. Analytical thinking, problem-solving skills, and effective communication set top performers apart in this role. These capabilities are crucial for developing, fine-tuning, and deploying RL models that solve complex, real-world problems efficiently.

What are some common challenges faced by professionals in reinforcement learning optimization roles, and how can they be addressed?

Professionals in Reinforcement Learning Optimization often encounter challenges such as sparse or delayed rewards, high computational requirements, and difficulty in ensuring model stability during training. Addressing these issues typically involves leveraging techniques like reward shaping, using experience replay buffers, and adopting robust exploration strategies. Collaborating closely with data engineers, software developers, and domain experts is also crucial to ensure that the RL models are well-integrated and perform reliably in production environments.

What other helpful pages are available for Reinforcement Learning Optimization?

Other pages related to Reinforcement Learning Optimization:

Infographic showing various Reinforcement Learning Optimization job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 74% Full Time, 23% Part Time, and 1% Contract. Highlights an 83% Physical, 2% Hybrid, and 15% Remote job distribution, with an average salary of $83,885 per year, or $40.3 per hour.

Helix AI Engineer, Reinforcement Learning

San Jose, CA • On-site

Figure
See more jobs at Figureexternal

$200K - $400K/yr

Full-time

Re-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 US base salary range for this full-time position is between $200,000 - $400,000

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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