1

Postdoctoral In Reinforcement Learning Jobs (NOW HIRING)

Reinforcement Learning (RL) Engineer Location: New York (Office) On-site | Full-time Compensation ... Our client operates primarily in-person . Benefits * High-Stakes Autonomy: Unmatched ownership over ...

Reinforcement Learning Engineer

New York, NY · On-site

$87K - $118K/yr

Reinforcement Learning (RL) Engineer Location: New York (Office) On-site | Full-time Compensation ... Our client operates primarily in-person . Benefits * High-Stakes Autonomy: Unmatched ownership over ...

next page

Showing results 1-20

Postdoctoral In Reinforcement Learning information

See salary details

$25K

$59K

$83.5K

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

As of Jul 12, 2026, the average yearly pay for postdoctoral in reinforcement learning in the United States is $59,022.00, according to ZipRecruiter salary data. Most workers in this role earn between $49,000.00 and $66,500.00 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.
More about Postdoctoral In Reinforcement Learning jobs
What cities are hiring for Postdoctoral In Reinforcement Learning jobs? Cities with the most Postdoctoral In Reinforcement Learning job openings:
What states have the most Postdoctoral In Reinforcement Learning jobs? States with the most job openings for Postdoctoral In Reinforcement Learning jobs include:
Infographic showing various Postdoctoral In Reinforcement Learning job openings in the United States as of July 2026, with employment types broken down into 1% As Needed, 73% Full Time, 21% Part Time, 1% Temporary, and 4% Contract. Highlights an 92% Physical, 1% Hybrid, and 7% Remote job distribution, with an average salary of $59,022 per year, or $28.4 per hour.
Machine Learning Engineer, Depot Automation

Machine Learning Engineer, Depot Automation

Waymo

Mountain View, CA

Other

Posted 3 hours ago


Job description

This role is at the intersection of robotics and machine learning, driving the next generation of operational efficiency for Waymo's rapidly expanding autonomous fleet. You will lead efforts to generalize complex depot operations-such as exterior cleaning, sensor calibration, and maintenance checks-using advanced robotics. Key work involves leveraging foundation models, reinforcement learning, simulation, and integrating ML models in production at scale. You will interface closely with operations teams to translate real-world needs into robust, working solutions.

This role follows a hybrid work schedule and reports to a Director, Hardware and Sensors.

You will:

  • Drive the next generation of operational efficiency for Waymo's rapidly expanding autonomous fleet
  • Contribute to accomplishing complex depot operations using advanced robotics
  • Focus on complex depot operations, such as charging, interior cleaning, vehicle inspection, and routine vehicle maintenance tasks
  • Leverage foundation models, reinforcement learning, and simulation
  • Integrate ML models in production at scale
  • Interface closely with operations teams to translate real-world needs into robust, working solutions

You have:

  • 3+ years of experience in training and evaluating large machine learning models
  • 3+ years of experience with robotics, preferably industrial robotics 
  • Expertise in reinforcement learning and its applications to real-world problems

We prefer:

  • A PhD in Machine Learning, Robotics, or a related technical field or equivalent experience 
  • Experience with applying machine learning techniques to large-scale industrial problems is a plus
  • Background in collaborating with internal and external research partners on applying ML to large-scale industry scale problems