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Postdoctoral In Reinforcement Learning Jobs in Taunton, MA

Klaviyo

Senior AI Engineer - Customer Agent

Boston, MA

$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 ...

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All JobsPostdoctoral In Reinforcement Learning JobsPostdoctoral In Reinforcement Learning Jobs in Taunton, MA

Postdoctoral In Reinforcement Learning information

See Taunton, MA salary details

$25.6K

$60.5K

$85.6K

How much do postdoctoral in reinforcement learning jobs pay per year?

As of Jul 30, 2026, the average yearly pay for postdoctoral in reinforcement learning in Taunton, MA is $60,528.00, according to ZipRecruiter salary data. Most workers in this role earn between $50,200.00 and $68,200.00 per year, depending on experience, location, and employer.

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 the key skills and qualifications needed to thrive as a Postdoctoral Researcher in Reinforcement Learning, and why are they important?

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 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.
Postdoctoral In Reinforcement Learning jobs near you
Infographic showing various Postdoctoral In Reinforcement Learning job openings in Taunton, MA as of June 2026, with employment types broken down into 60% Full Time, 35% Part Time, and 5% Nights. Highlights an 92% Physical, 1% Hybrid, and 7% Remote job distribution, with an average salary of $60,528 per year, or $29.1 per hour.

Reinforcement Learning Engineer - Locomanipulation

Humanoid

Boston, MA • On-site

$200K - $350K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 8 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 HMND‑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 US‑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 in‑office.
  • Collaboration with top‑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.

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