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Reinforcement Learning Engineer Jobs (NOW HIRING)

Centific

Applied Reinforcement Learning Engineer

Applied Reinforcement Learning Engineer Location: Palo Alto, CA or Seattle, WA (Hybrid/Remote) About the Team Centific AI Research advances foundational AI models and applications through ...

Skild AI

Machine Learning Engineer

San Mateo, CA · On-site

Position Overview We are looking for a Machine Learning Engineer to be responsible for designing and implementing cutting-edge reinforcement learning algorithms, conducting experiments, and ...

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How much do reinforcement learning engineer jobs pay per year?

As of Sep 8, 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 is a reinforcement learning engineer?

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 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 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 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 September 2026, with employment types broken down into 83% Full Time, and 17% Contract. Highlights an 78% In-person, and 22% Remote job distribution, with an average salary of $115,864 per year, or $55.7 per hour.

Reinforcement Learning Engineer, Grasping

Houston, TX • On-site

Persona AI, Inc.
See more jobs at Persona AI, Inc.external

$100 - $125/hr

Other

Medical, PTO

Re-posted 22 days ago

Job description

Persona AI is developing and commercializing rugged, multi-purpose humanoid robots that perform real work. Persona's founding team has a decades-long history in humanoid robotics, bionics, and product development delivering robust hardware that has touched the stars, worked miles below the surface of the ocean, roamed Disney Parks, and has even been featured on a US postage stamp. Our mission is focused squarely on shipping beautiful, reliable products at massive scale, while building a customer-focused team to achieve these aims.

Role Overview

We are looking for a Reinforcement Learning Engineer to join our Manipulation team, focused on dexterous grasping. Our goal is to ship capable, reliable grasping policies on real hardware with high-DOF robotic hands. We are looking for someone who can follow recent advances in reinforcement learning and related learning-based methods, judge what is practically useful, and adapt those ideas on our platform. If you are earlier in your career but exceptional, we want to hear from you; equally, a more experienced candidate who brings deep RL expertise will thrive here.

Your Role
  • Train and iterate on reinforcement learning policies for complex grasping tasks including functional grasping, tool use, in-hand manipulation, and environment interaction.
  • Implement and refine sim-to-real transfer pipelines to bridge the gap between simulation and physical robotic hand performance.
  • Develop reward functions, curriculum strategies, and training environments in MuJoCo and Isaac Lab.
  • Run experiments on real robots alongside simulation, evaluating and debugging policy behavior on hardware.
  • Monitor, evaluate, and adapt state-of-the-art research in learning-based grasping to deploy on our humanoid platform.
  • Collaborate with the rest of the software team to deploy end-to-end grasping systems.
  • Benchmark and evaluate grasp policies across object diversity, clutter scenes, and real-world uncertainties.
  • Integrate tactile sensing and feedback into grasp policies for robust, force‑aware manipulation.
We're Looking For
  • BS, MS, or PhD in Robotics, Computer Science, Machine Learning, or a related field.
  • 2+ years of hands‑on experience in reinforcement learning for robotic manipulation; exceptional recent graduates from relevant research labs will be considered.
  • Demonstrated ability to read, understand, and implement ideas from recent robotics and machine learning research.
  • Hands‑on experience training RL agents for robotic manipulation tasks, including reward shaping and policy evaluation.
  • Experience with sim‑to‑real transfer: domain randomization, physics tuning, or real‑world policy validation on hardware.
  • Proficiency in Python and deep learning frameworks (PyTorch, JAX), along with RL libraries such as rsl_rl or skrl.
  • Experience preparing meshes and collision geometries for RL environments in simulators such as MuJoCo and/or Isaac Sim.
Bonus Qualifications
  • Experience deploying RL‑trained policies on physical robotic hands.
  • Experience with tactile sensors and integrating tactile feedback into learned grasp policies.
  • Experience with contact‑rich manipulation and force/torque estimation.
  • Familiarity with other learning‑based approaches such as behavior cloning, imitation learning, or diffusion‑based policy methods.
  • Publications or project work at top‑tier venues (CoRL, RSS, ICRA) on grasping or dexterous manipulation.
  • Experience in a humanoid robot startup environment.
Why Join Persona AI?
  • We offer competitive compensation, a performance‑based bonus, 99% employer covered medical benefits, early‑stage equity, competitive PTO, and a company‑wide paid winter break between December 24th and January 2nd.
  • You’ll shape technology that’s redefining the possibilities of robotics and human interaction.
  • Work alongside passionate teammates who value creativity, collaboration, and continuous learning.
  • Enjoy full access to advanced tools, hardware labs, and the freedom to push the boundaries of what robots can do.
  • Persona AI is an Equal Opportunity Employer.
  • All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, age, disability, veteran status, or any other characteristic protected by applicable federal, state, or local law.
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