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Freelance Deep Reinforcement Learning Jobs (NOW HIRING)

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 ...

MLabs

Reinforcement Learning Engineer

New York, NY

$87K - $118K/yr

Reinforcement Learning (RL) Engineer Location: New York (Office) On-site | Full-time Compensation ... A deep-dive assessment into RL architecture, simulation frameworks, and live production experience.

poolside

Member of Engineering (Reinforcement Learning)

$99K - $136K/yr

What unites us is our deep care for what we build together. We're in a race that requires hard work ... ABOUT THE ROLE You would be working on our reinforcement learning team focused on improving ...

MLabs

Reinforcement Learning Engineer

New York, NY · On-site

$87K - $118K/yr

Reinforcement Learning (RL) Engineer Location: New York (Office) On-site | Full-time Compensation ... A deep-dive assessment into RL architecture, simulation frameworks, and live production experience.

MLabs

Reinforcement Learning Engineer

New York, NY · On-site

$87K - $118K/yr

Reinforcement Learning (RL) Engineer Location: New York (Office) On-site Full-time Compensation ... A deep-dive assessment into RL architecture, simulation frameworks, and live production experience.

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How much do freelance deep reinforcement learning jobs pay per hour?

As of Jul 12, 2026, the average hourly pay for freelance deep reinforcement learning in the United States is $47.71, according to ZipRecruiter salary data. Most workers in this role earn between $24.28 and $61.78 per hour, depending on experience, location, and employer.

Which 5 jobs will survive AI?

For a Freelance Deep Reinforcement Learning specialist, jobs that require complex problem-solving, creativity, and human judgment are more likely to survive AI automation. These include roles like AI research scientist, data scientist, machine learning engineer, AI ethics consultant, and technical project manager. Such positions often involve specialized skills, domain expertise, and adaptability that are difficult for AI to fully replicate.

Do ML engineers need a PhD?

ML engineers, including those working with deep reinforcement learning, do not typically require a PhD; many have bachelor's or master's degrees in computer science, data science, or related fields. Practical experience, strong programming skills, and knowledge of machine learning frameworks are often more important than advanced degrees. However, a PhD can be beneficial for research-focused roles or specialized positions in the field.

Will AI replace deep learning?

Deep reinforcement learning is a subset of AI that combines deep learning with reinforcement learning techniques. While AI continues to advance, deep learning and deep reinforcement learning are complementary tools used by professionals in the field; AI is unlikely to fully replace these specialized methods but will evolve alongside them. Job roles in this area require knowledge of neural networks, programming skills, and familiarity with AI frameworks like TensorFlow or PyTorch.

Is ML a high paying job?

Freelance deep reinforcement learning specialists often command high rates due to the specialized skills required, such as expertise in neural networks and programming in Python or TensorFlow. Compensation varies based on experience, project complexity, and client budgets, but experienced professionals in this field can earn competitive or high salaries compared to other freelance tech roles.
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Freelance Deep Reinforcement Learning jobs near you
Infographic showing various Freelance Deep Reinforcement Learning job openings in the United States as of July 2026, with employment types broken down into 20% Internship, 20% Full Time, 40% Part Time, and 20% Contract. Highlights an 60% In-person, and 40% Remote job distribution, with an average salary of $99,230 per year, or $47.7 per hour.
Research Engineer, Machine Learning (Reinforcement Learning)

Research Engineer, Machine Learning (Reinforcement Learning)

Anthropic

San Francisco, CA • On-site

$241K/yr

Other

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