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Reinforcement Learning Engineer Jobs in Cambridge, MA

Analogdevices

Principal ML Engineer (Robotics)

Boston, MA · On-site

$200K - $275K/yr

Principal Machine Learning Engineer Target hire: Feb 2026 The Dexterous AI Group (DAG) is seeking ... Strong background in ML/AI, including Reinforcement Learning and Imitation Learning. * Expertise in ...

Tutor

Senior Robotics Software Engineer

Watertown, MA · On-site

$133K - $175K/yr

... Reinforcement learning • Proficiency programming in a Python-Linux environment • Comfort with programming linters (Flake8, Mypy) • Software support of real-time systems • Visualization of ...

Career Site

Lead AI Engineer

Quincy, MA · On-site

$180K - $280K/yr

Developing and applying reinforcement learning strategies to optimize and automate decision-making ... Strong programming skills in Python, familiarity with libraries/frameworks such as PyTorch ...

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All JobsReinforcement Learning Engineer JobsReinforcement Learning Engineer Jobs in Cambridge, MA

Reinforcement Learning Engineer information

See Cambridge, MA salary details

$41.5K

$126.6K

$209.3K

How much do reinforcement learning engineer jobs pay per year?

As of Aug 21, 2026, the average yearly pay for reinforcement learning engineer in Cambridge, MA is $126,637.00, according to ZipRecruiter salary data. Most workers in this role earn between $90,700.00 and $165,600.00 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.

What are popular job titles related to Reinforcement Learning Engineer jobs in Cambridge, MA?

For Reinforcement Learning Engineer jobs in Cambridge, MA, the most frequently searched job titles are:

What job categories do people searching Reinforcement Learning Engineer jobs in Cambridge, MA look for?

The top searched job categories for Reinforcement Learning Engineer jobs in Cambridge, MA are:

Reinforcement Learning Engineer jobs near you

Machine Learning / Computer Vision Engineer

Eka

Boston, MA • On-site

$90 - $130/hr

Other

Posted 15 days ago

Job description

Eka Robotics

Eka Robotics is on a mission to build intelligence for the physical world - robots that are fast, general, and reliable. Our approach, grounded in physics, unlocks superhuman capabilities. We are defining the frontier of robotics research and deployment.

Our team consists of pioneers in robotics and machine learning. We are now hiring to scale our R&D effort. We are looking for hands‑on individuals who are excited to help shape the future of robotics.

Responsibilities
  • Build computer vision and visual representation learning pipelines for robotic manipulation, including RGB, RGB‑D, depth, segmentation, pose, keypoint, and object‑centric representations.
  • Develop visual models that support reinforcement learning and imitation learning policies, including end‑to‑end visuomotor policies that map visual observations to robot actions.
  • Improve our data pipeline for vision‑based manipulation policies through domain randomization, photorealistic rendering, synthetic data generation, sensor noise modeling, and real‑world fine‑tuning.
  • Design and train perception models that are robust to lighting changes, camera viewpoint shifts, texture variation, clutter, occlusion, object instance variation, and imperfect calibration.
  • Evaluate learned visual representations and policies on real robotic manipulation tasks, identify failure modes, and iterate on models, data, and training procedures.
  • Collaborate with robotics, robot learning, and simulation engineers to define the perception strategy for robotic manipulation.
  • Set up, calibrate, and evaluate camera and depth sensing systems when needed, with an emphasis on how sensor choices affect learned policies and real‑world robustness.
Minimum Qualifications
  • Ph.D. in computer vision or 3+ years of experience working on a computer vision product.
  • Strong background in machine learning for computer vision, especially deep learning‑based visual perception.
  • Experience training modern computer vision models in JAX, PyTorch or similar frameworks.
  • Practical experience with visual representation learning, object detection, segmentation, pose estimation, depth estimation, tracking, or 3D perception.
  • Strong Python programming skills.
  • Ability to move fluidly between research code and production‑quality systems.
  • Strong understanding of how data distribution, sensor noise, calibration, lighting, and scene variation affect model performance.
Preferred Qualifications
  • Experience training policies from visual observations, including RGB, RGB‑D, point clouds, object‑centric representations, or learned latent representations.
  • Experience with domain randomization, synthetic data generation, differentiable rendering, neural rendering, or photorealistic simulation.
  • Experience with robotics simulators or synthetic data tools such as Isaac Sim, MuJoCo or similar environments.
  • Familiarity with robot learning methods such as reinforcement learning, behavior cloning, diffusion policies, offline RL, or learning from demonstrations.
  • Experience with real robot deployment, including camera calibration, hand‑eye calibration, depth sensors, ROS/ROS2, or robot data collection pipelines.
  • First‑author publications in top computer vision, robotics, or machine learning venues such as CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML, RSS, CoRL, ICRA, or IROS.
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