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Reinforcement Learning Engineer Jobs (NOW HIRING)

Mariana Minerals

Machine Learning Engineer

Ann Arbor, MI · On-site

$120K - $160K/yr

Our internal platform uses the same reinforcement learning toolkits that power self-driving ... Engineer Out Requirements, then Automate - We simplify, optimize, and then automate for scale.

Full Scope

Machine Learning Engineer

Reston, VA · On-site

Machine Learning Engineer Location: Fort Meade, MD Required Clearance : TS/SCI w/ Full-Scope Poly ... reinforcement learning, and deep learning. * Experience with data processing tools like Pandas ...

Human Archive

Machine Learning Engineer

San Francisco, CA · On-site

$100K - $150K/yr

Build post-training and reinforcement learning systems around robotics failure modes and corrective ... Engineers who want their work to directly impact the next frontier of physical AGI * Strong ML ...

Apple

Machine Learning Engineer

Cupertino, CA · On-site

As a Machine Learning Engineer on our core AI/ML team, you will design and build GenAI-powered ... and reinforcement learning Solid understanding of core machine learning concepts, including ...

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Reinforcement Learning Engineer information

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$38K

$115.9K

$191.5K

How much do reinforcement learning engineer jobs pay per year?

As of Jul 9, 2026, the average yearly pay for reinforcement learning engineer in the United States is $115,864.00, according to ZipRecruiter salary data. Most workers in this role earn between $83,000.00 and $151,500.00 per year, depending on experience, location, and employer.

What are Reinforcement Learning Engineers?

Reinforcement Learning Engineers are specialized professionals who design, develop, and implement algorithms based on reinforcement learning, a type of machine learning where agents learn to make decisions by receiving rewards or penalties. They work on building models that enable machines to learn optimal actions through trial and error in complex environments. Their responsibilities often include developing RL architectures, tuning hyperparameters, running simulations, and applying RL methods to real-world problems like robotics, gaming, or recommendation systems. RL Engineers typically have strong backgrounds in computer science, mathematics, and deep learning, along with experience in programming languages like Python and frameworks such as TensorFlow or PyTorch.

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

To thrive as a Reinforcement Learning Engineer, you need a strong background in machine learning, mathematics (especially probability and statistics), and programming languages like Python, often supported by a relevant degree in computer science or engineering. Familiarity with deep learning frameworks (such as TensorFlow or PyTorch), RL libraries (like OpenAI Gym), and cloud computing platforms is typically required. Problem-solving skills, creativity, and effective collaboration help set outstanding engineers apart in this field. These competencies enable the design and deployment of advanced RL solutions that address real-world challenges and drive innovation.

What are some common challenges faced by Reinforcement Learning Engineers when deploying models in real-world environments?

One of the main challenges Reinforcement Learning (RL) Engineers face is bridging the gap between simulation and real-world deployment. Models that perform well in controlled environments may struggle with unpredictable data, safety constraints, or limited feedback in production. Additionally, RL algorithms often require significant computational resources and careful tuning to avoid instability. Collaboration with domain experts and software engineers is essential to address these issues and ensure successful integration of RL solutions into existing systems.

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

AspectReinforcement Learning EngineerMachine Learning Engineer
CredentialsBachelor's/Master's in CS, AI, or related; experience with RL frameworksBachelor's/Master's in CS, Data Science, or related; experience with ML algorithms
Work EnvironmentResearch labs, AI startups, tech companies focusing on RL applicationsTech companies, data-driven firms, AI departments across industries
Industry UsageSpecialized in RL projects like robotics, game AI, autonomous systemsBroader applications including predictive modeling, NLP, computer vision

Reinforcement Learning Engineers focus on developing algorithms that learn through interactions with environments, often in robotics or gaming. Machine Learning Engineers work on a wider range of models and applications. While both roles require strong programming and math skills, RL Engineers specialize in sequential decision-making, whereas ML Engineers handle diverse data-driven tasks across industries.

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Reinforcement Learning Engineer jobs near you
Infographic showing various Reinforcement Learning Engineer job openings in the United States as of July 2026, with employment types broken down into 95% Full Time, 2% Part Time, and 3% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution, with an average salary of $115,864 per year, or $55.7 per hour.
Research Engineer, Code RL (Reinforcement Learning)

Research Engineer, Code RL (Reinforcement Learning)

Anthropic

San Francisco, CA • On-site, Remote

$241K/yr

Other

Posted 28 days ago

Job description

About the RL Teams

Our Reinforcement Learning teams play 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 our latest Claude models. 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

We're hiring for the Code RL team within the RL organization. As a Research Engineer, you'll advance our models' ability to write, edit, test, debug, and ship real software - end to end, on real codebases, with real tools - and to do it correctly, fast, and safely.

This role blends research and engineering. You'll design RL environments and coding tasks, build the reward signals and verifiers that capture what "good code" means, run training experiments on frontier models, diagnose why a model does (or doesn't) get better at a class of software-engineering work, and improve the speed and reliability of the pipelines that make all of that iterate fast. Code RL spans several focus areas - from agentic coding behaviors and code correctness, to long-horizon autonomous engineering, to high-performance code for accelerators - and we'll match you to the area where you'll have the most impact.

You may be a good fit if you:

  • Have strong software-engineering skills and deep Python expertise, including async/concurrent programming

  • Are comfortable owning systems end to end and debugging across the stack

  • Can balance research exploration with engineering implementation, and engage rigorously in shaping experimental design and interpreting results

  • Care about code quality, testing, and performance

  • Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems

Strong candidates may also have:

  • Experience with reinforcement learning, RLHF, post-training, or LLM finetuning

  • Built coding agents, code-execution sandboxes, eval harnesses, verifiers, or developer tooling

  • Background in program analysis, testing, verification, compilers, or formal methods

  • Experience with PyTorch and large-scale distributed training; performance profiling and optimization of ML systems

  • CUDA / GPU or TPU kernel experience and accelerator-performance intuition

  • Experience with virtualization and sandboxed code execution environments

Related roles

If your background leans toward one of these areas specifically, you may also want to look at these postings:

  • Research Engineer, Performance RL (Reinforcement Learning) - teaching Claude to write correct, fast code for accelerators

  • Research Engineer, Universes - long-horizon, ultra-realistic agentic training environments

  • Research Engineer, Cybersecurity RL (Reinforcement Learning) - RL for security-relevant coding capabilities

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