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Reinforcement Learning Engineer Jobs (NOW HIRING)

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Member of Engineering (Reinforcement Learning)

$99K - $136K/yr

Reinforcement Learning, LLMs and reasoning models * Ability to discuss the latest research with sufficient level of detail * Is reasonably opinionated * Engineering skills * Strong machine learning ...

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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 Jun 19, 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.

More about Reinforcement Learning Engineer jobs
What cities are hiring for Reinforcement Learning Engineer jobs? Cities with the most Reinforcement Learning Engineer job openings:
What states have the most Reinforcement Learning Engineer jobs? States with the most job openings for Reinforcement Learning Engineer jobs include:
What job categories do people searching Reinforcement Learning Engineer jobs look for? The top searched job categories for Reinforcement Learning Engineer jobs are:
Reinforcement Learning Engineer jobs near you
Infographic showing various Reinforcement Learning Engineer job openings in the United States as of June 2026, with employment types broken down into 100% Full Time. Highlights an 87% Physical, 5% Hybrid, and 8% Remote job distribution, with an average salary of $115,864 per year, or $55.7 per hour.

Machine Learning Engineer: Imitation and Reinforcement Learning for Robotics

Bedrock Robotics

San Francisco, CA • On-site

Full-time

Posted 11 days ago

Job description

Join the team bringing advanced autonomy to the built world
At Bedrock, we're moving AI out of the lab and into the real world. Our team is composed of industry veterans who helped launch Waymo, scaled Segment to a $3.2B acquisition, and grew Uber Freight to $5B in revenue. Today, we're deploying autonomous systems on heavy construction machinery across the country, accelerating project schedules of billion-dollar infrastructure projects and improving safety on job sites. Backed by $350M in funding, we're working quickly to close the gap between America's surging demand for housing, data centers, manufacturing hubs, and the construction industry's growing labor shortage.
This is where algorithms meet steel-toed boots. You'll collaborate with construction veterans and world-class engineers to solve physical-world problems that simulations can't touch. If you're ready to apply cutting-edge technology to solve meaningful problems alongside a talented team-we'd love to have you join us.
We're looking for a Machine Learning Engineer with a focus on behavior learning, specifically data-driven behavior policies and robust data infrastructure. In this role, you'll be responsible for developing and scaling state-of-the-art learning architectures, while also building the data systems that make these models reliable, scalable, and reproducible in production.
What You'll Do:
  • Design, train, validate, and launch models for behavior cloning and reinforcement learning
  • Build and maintain data ingestion, labeling, and management pipelines to ensure high-quality training datasets
  • Build metrics to evaluate model performance in open loop, simulation, and in the real world
  • Collaborate with simulation, systems, and infrastructure teams to integrate ML models into real-world autonomous systems
  • Deploy and debug these models in real-world environments, addressing practical issues such as latency, hardware constraints, and system integration

What We're Looking For:
  • 3+ years of practical experience applying Machine Learning with Deep Learning frameworks, such as PyTorch/Tensorflow/JAX to solve real-world problems
  • 3+ years of professional experience building, deploying, and maintaining Machine Learning models in production environments
  • Familiarity with recent literature and methods in learned behavior policies
  • Practical experience in behavior cloning and/or reinforcement learning
  • Bonus: Experience with diffusion policies, Vision-Language-Action (VLA) models, or related technologies
  • Bonus: Published work in conferences such as ICRA, IROS, CoRL, CVPR, ECCV, ICCV, ICML, NeurIPS, ...

Our roles are often flexible. If you don't fit all the criteria, or are in another location (especially one where we have an office like SF or NY) please apply anyway! We'd love to consider you.

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