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Postdoctoral In Reinforcement Learning Jobs in Massachusetts

Eka

Machine Learning / Computer Vision Engineer

Boston, MA · On-site

$121K - $142K/yr

Our approach, grounded in physics, unlocks superhuman capabilities. We are defining the frontier of ... Develop visual models that support reinforcement learning and imitation learning policies ...

Tufts University

Postdoctoral Scholar

Medford, MA · On-site

$67K/yr

Overview Postdoctoral Scholar in Machine Learning for Physical Systems The Department of Electrical and Computer Engineering at Tufts University invites applications for a Postdoctoral Scholar in the ...

Klaviyo

Senior AI Engineer - Customer Agent

Boston, MA · On-site

$113K - $155K/yr

Experience in reinforcement learning. We use Covey as part of our hiring and / or promotional process. For jobs or candidates in NYC, certain features may qualify it as an AEDT. As part of the ...

FieldAI

Senior Research Scientist - Manipulation

Boston, MA · On-site

$107K - $136K/yr

In this role, you will develop new methods for learned and physically-grounded models, spanning reinforcement-learning, imitation learning, multimodal representation learning, cross-embodiment ...

FieldAI

Senior Research Scientist - Manipulation

Boston, MA · On-site

$107K - $136K/yr

In this role, you will develop new methods for learned and physically-grounded models, spanning reinforcement-learning, imitation learning, multimodal representation learning, cross-embodiment ...

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Postdoctoral In Reinforcement Learning information

What is a postdoctoral researcher in reinforcement learning?

A Postdoctoral Researcher in Reinforcement Learning is an individual who has completed a PhD and conducts advanced research in the field of reinforcement learning, a branch of artificial intelligence focused on how agents take actions in environments to maximize rewards. These researchers often work in academic, industrial, or governmental research settings, collaborating on projects that advance the theoretical foundations or practical applications of reinforcement learning. Their responsibilities may include designing experiments, developing algorithms, publishing papers, and mentoring graduate students.

What are the key skills and qualifications needed to thrive as a postdoctoral researcher in reinforcement learning?

To thrive as a Postdoctoral Researcher in Reinforcement Learning, you need a PhD in computer science or a related field, with deep expertise in machine learning, statistics, and algorithm development. Proficiency in programming languages such as Python, experience with deep learning frameworks (e.g., TensorFlow or PyTorch), and familiarity with reinforcement learning libraries are typically required. Strong analytical thinking, problem-solving ability, collaboration, and scientific communication skills help you excel in research teams and publish impactful work. These competencies are vital to advancing state-of-the-art research, developing novel algorithms, and contributing to the academic and industrial progress in AI.

What are some common challenges faced by postdoctoral researchers in reinforcement learning, and how can they be addressed?

Postdoctoral researchers in reinforcement learning often face challenges such as balancing independent research projects with collaborative work, staying up-to-date with rapidly evolving literature, and managing the pressure to publish in top conferences. Effective time management, regular engagement with the research community through seminars and workshops, and seeking mentorship from senior colleagues can help address these challenges. Additionally, collaborating with interdisciplinary teams can offer fresh perspectives and support, making it easier to navigate complex research problems.

What is the difference between Postdoctoral In Reinforcement Learning vs Postdoctoral In Machine Learning?

AspectPostdoctoral In Reinforcement LearningPostdoctoral In Machine Learning
Required CredentialsPhD in Computer Science, AI, or related field; strong programming skills; research experience in reinforcement learningPhD in Computer Science, AI, or related field; strong programming skills; research experience in machine learning
Work EnvironmentAcademic labs, research institutions, industry R&D teams focused on reinforcement learning applicationsAcademic labs, research institutions, industry R&D teams working on various machine learning techniques
Industry UsagePrimarily in AI research, robotics, gaming, and autonomous systemsBroader applications including data analysis, predictive modeling, and AI research

Postdoctoral In Reinforcement Learning specializes in research related to decision-making algorithms and autonomous systems, whereas Postdoctoral In Machine Learning covers a wider range of AI techniques. Both roles require similar credentials but differ in focus and application areas.

What are popular job titles related to Postdoctoral In Reinforcement Learning jobs in Massachusetts?

For Postdoctoral In Reinforcement Learning jobs in Massachusetts, the most frequently searched job titles are:

What job categories do people searching Postdoctoral In Reinforcement Learning jobs in Massachusetts look for?

The top searched job categories for Postdoctoral In Reinforcement Learning jobs in Massachusetts are:

What cities in Massachusetts are hiring for Postdoctoral In Reinforcement Learning jobs?

Cities in Massachusetts with the most Postdoctoral In Reinforcement Learning job openings:

Postdoctoral In Reinforcement Learning jobs near you
Infographic showing various Postdoctoral In Reinforcement Learning job openings in Massachusetts as of June 2026, with employment types broken down into 47% Full Time, 38% Part Time, 5% Temporary, 5% Contract, and 5% Nights. Highlights an 92% Physical, 1% Hybrid, and 7% Remote job distribution.

Machine Learning / Computer Vision Engineer

Boston, MA • On-site

Eka
See more jobs at Ekaexternal

$121K - $142K/yr

Other

This job post has expired today. Applications are no longer accepted.

Key responsibilities

  • Build computer vision and visual representation learning pipelines for robotic manipulation.

  • Develop visual models that support reinforcement learning and imitation learning policies, including end-to-end visuomotor policies.

  • Evaluate learned visual representations and policies on real robotic manipulation tasks, identify failure modes, and iterate on models, data, and training procedures.

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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