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Reinforcement Learning Optimization Jobs (NOW HIRING)

Apptronik

Reinforcement Learning Engineer

Austin, TX · On-site

As a Reinforcement Learning Engineer, you will be a core contributor to the intelligence and ... You will work alongside a multidisciplinary team to develop high-performance policies and optimized ...

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 ...

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 ...

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Reinforcement Learning Optimization information

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$11K

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$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.

Research Engineer, Machine Learning (Reinforcement Learning)

San Francisco, CA • On-site

Anthropic
Software Development • 11 - 50 employees
Learn more about Anthropicexternal

$241K/yr

Full-time

Re-posted 6 days ago

Job description

About the teams

Our Reinforcement Learning teams lead Anthropic's reinforcement learning research and development, playing a critical role in advancing our AI systems. We've contributed to all Claude models, with significant impacts on the autonomy and coding capabilities of Claude Sonnet 4.5 and Opus 4.5. Our work spans several key areas:

  • Developing systems that enable models to use computers effectively
  • Advancing code generation through reinforcement learning
  • Pioneering fundamental RL research for large language models
  • Building scalable RL infrastructure and training methodologies
  • Enhancing model reasoning capabilities

We collaborate closely with Anthropic's alignment and frontier red teams to ensure our systems are both capable and safe. We partner with the applied production training team to bring research innovations into deployed models, and are dedicated to implement our research at scale. Our Reinforcement Learning teams sit at the intersection of cutting-edge research and engineering excellence, with a deep commitment to building high-quality, scalable systems that push the boundaries of what AI can accomplish.

About the Role

As a Research Engineer within Reinforcement Learning, you will collaborate with a diverse group of researchers and engineers to advance the capabilities and safety of large language models. This role blends research and engineering responsibilities, requiring you to both implement novel approaches and contribute to the research direction. You'll work on fundamental research in reinforcement learning, creating 'agentic' models via tool use for open-ended tasks such as computer use and autonomous software generation, improving reasoning abilities in areas such as mathematics, and developing prototypes for internal use, productivity, and evaluation.

Representative projects:
  • Architect and optimize core reinforcement learning infrastructure, from clean training abstractions to distributed experiment management across GPU clusters. Help scale our systems to handle increasingly complex research workflows.
  • Design, implement, and test novel training environments, evaluations, and methodologies for reinforcement learning agents which push the state of the art for the next generation of models.
  • Drive performance improvements across our stack through profiling, optimization, and benchmarking. Implement efficient caching solutions and debug distributed systems to accelerate both training and evaluation workflows.
  • Collaborate across research and engineering teams to develop automated testing frameworks, design clean APIs, and build scalable infrastructure that accelerates AI research.
You may be a good fit if you:
  • Are proficient in Python and async/concurrent programming with frameworks like Trio
  • Have experience with machine learning frameworks (PyTorch, TensorFlow, JAX)
  • Have industry experience in machine learning research
  • Can balance research exploration with engineering implementation
  • Enjoy pair programming (we love to pair!)
  • Care about code quality, testing, and performance
  • Have strong systems design and communication skills
  • Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems
Strong candidates may have:
  • Familiarity with LLM architectures and training methodologies
  • Experience with reinforcement learning techniques and environments
  • Experience with virtualization and sandboxed code execution environments
  • Experience with Kubernetes
  • Experience with distributed systems or high-performance computing
  • Experience with Rust and/or C++
Strong candidates need not have:
  • Formal certifications or education credentials
  • Academic research experience or publication history

Deadline to apply: None. Applications will be reviewed on a rolling basis. 

Apply