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Reinforcement Learning Engineer Jobs in Massachusetts

Senior Machine Learning Engineer, Data Mining

Boston, MA ยท On-site +1

$133K - $175K/yr

Reinforcement Learning for Data Discover : Build RL-based policy learning and reasoning systems for ... Collaborate with backend engineers to deploy distilled and RL models into production. Optimize for ...

Senior Machine Learning Engineer, Data Mining

Boston, MA ยท On-site +1

$133K - $175K/yr

Reinforcement Learning for Data Discover : Build RL-based policy learning and reasoning systems for ... Collaborate with backend engineers to deploy distilled and RL models into production. Optimize for ...

Software Machine Learning Engineer

Reading, MA ยท On-site

$116K - $186K/yr

Opportunity Overview As a Machine Learning Engineer, you will design, develop, and deploy applied ... Build and experiment with reinforcement learning algorithms (e.g., policy gradients, PPO, Q ...

Senior Robot Learning Engineer

Boston, MA ยท On-site

$144K - $198K/yr

Senior Robot Learning Engineer The Dexterous AI Group (DAG) is looking for Robot Learning Engineers ... Reinforcement Learning and Imitation Learning for manipulation. * Planning and control algorithms ...

Senior Robot Learning Engineer

Boston, MA ยท On-site

$144K - $198K/yr

Senior Robot Learning Engineer The Dexterous AI Group (DAG) is looking for Robot Learning Engineers ... Reinforcement Learning and Imitation Learning for manipulation. * Planning and control algorithms ...

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Reinforcement Learning Engineer information

See Massachusetts salary details

$41.5K

$126.5K

$209.1K

How much do reinforcement learning engineer jobs pay per year?

As of Jul 26, 2026, the average yearly pay for reinforcement learning engineer in Massachusetts is $126,538.00, according to ZipRecruiter salary data. Most workers in this role earn between $90,600.00 and $165,500.00 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.

What cities in Massachusetts are hiring for Reinforcement Learning Engineer jobs? Cities in Massachusetts with the most Reinforcement Learning Engineer job openings:
Infographic showing various Reinforcement Learning Engineer job openings in Massachusetts as of July 2026, with employment types broken down into 71% Full Time, and 29% Contract. Highlights an 67% In-person, and 33% Remote job distribution, with an average salary of $126,538 per year, or $60.8 per hour.

Reinforcement Learning Engineer - Locomanipulation

Humanoid

Boston, MA โ€ข On-site

$200K - $350K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 4 days ago


Job description

Here at Humanoid, we believe in a future where robots amplify human potential. That's why we've set out on a mission to build the world's most capable, commercially-scalable, and safe humanoid robots. We're bringing that mission to life with HMNDโ€‘01 Alpha - our rapidly developed humanoid platform now running in real industrial pilots - and we're growing the team to take it even further.
About the Role
We are looking for a Senior or Staff Reinforcement Learning Engineer to develop learning-based control policies for humanoid robots.
You will design and train reinforcement learning policies that enable dynamic locomotion and loco-manipulation behaviors on real robots. Your work will focus on building scalable training pipelines, designing reward functions and environments, and improving sim-to-real transfer for reliable deployment on hardware.
You will work closely with control and robotics engineers to integrate learned policies into the robot control stack, ensuring stable and robust behavior in real-world conditions.
Development will involve continuous iteration between large-scale simulation and hardware experiments.
The problems you will work on include dynamic locomotion, balance recovery, contact-rich manipulation, and multi-behavior policy learning.
What You'll Do
  • Design and train reinforcement learning policies for humanoid robot control.
  • Build scalable simulation and training pipelines (e.g., Isaac Lab, MuJoCo).
  • Design reward functions, observation spaces, and curricula for complex behaviors.
  • Improve robustness and sim-to-real transfer of learned policies.
  • Deploy and evaluate policies on real robotic systems.
  • Integrate policies into the control stack.

What We're Looking For
  • MS or PhD in Robotics, Machine Learning, Computer Science, or related field.
  • Strong experience with reinforcement learning (e.g., PPO, SAC, offline RL).
  • Experience applying RL to robotics or physical systems.
  • Experience deploying learned policies on real robotic systems.
  • Experience with physics-based simulation environments (e.g., Isaac Lab, MuJoCo).
  • Strong programming skills in Python and/or C++.
Nice to have
  • Experience with RL for locomotion or legged robots.
  • Experience with sim-to-real transfer.
  • Familiarity with robot dynamics, control, or whole-body control.

What We Offer
  • Comprehensive health coverage for USโ€‘based employees, including fully paid medical, dental, and vision insurance, with virtual care and employee assistance resources.
  • Meaningful time off to rest and recharge: 23 days of PTO (accrued), separate sick leave, and paid company holidays.
  • 401(k) retirement plan with employer match.
  • Equity included-we believe builders should share in what they build.
  • Free daily catered lunch, snacks, and drinks inโ€‘office.
  • Collaboration with topโ€‘tier engineers, researchers, and product experts in AI and robotics.
  • Freedom to influence the product and own key initiatives.

For this role in Massachusetts, the expected base salary range is $200K-$350K USD per year; your placement in that range depends on how your experience maps to our internal leveling.