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Postdoctoral In Reinforcement Learning Jobs in Chicago, IL

Braze

Senior Lead AI Solutions Consultant

Chicago, IL · On-site

$124K - $186K/yr

An expert in machine learning: You have a solid grasp of machine learning, including a familiarity with reinforcement learning * Mar-tech savvy: You understand the marketing technology ecosystem and ...

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Postdoctoral In Reinforcement Learning information

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$25.8K

$60.8K

$86K

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

As of Jul 14, 2026, the average yearly pay for postdoctoral in reinforcement learning in Chicago, IL is $60,801.00, according to ZipRecruiter salary data. Most workers in this role earn between $50,500.00 and $68,500.00 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.
What are popular job titles related to Postdoctoral In Reinforcement Learning jobs in Chicago, IL? For Postdoctoral In Reinforcement Learning jobs in Chicago, IL, the most frequently searched job titles are:
What job categories do people searching Postdoctoral In Reinforcement Learning jobs in Chicago, IL look for? The top searched job categories for Postdoctoral In Reinforcement Learning jobs in Chicago, IL are:
What cities near Chicago, IL are hiring for Postdoctoral In Reinforcement Learning jobs? Cities near Chicago, IL 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 Chicago, IL as of July 2026, with employment types broken down into 1% As Needed, 75% Full Time, 20% Part Time, 1% Temporary, and 3% Contract. Highlights an 92% Physical, 1% Hybrid, and 7% Remote job distribution, with an average salary of $60,801 per year, or $29.2 per hour.
Autonomous Infrastructure and Robotic Science Lead

Autonomous Infrastructure and Robotic Science Lead

Argonne National Laboratory

Lemont, IL • On-site

Full-time

Posted 26 days ago

Job description

The Computing, Environment, and Life Sciences (CELS) Directorate seeks an outstanding scientist to lead and support frontier research at the intersection of AI, autonomous platforms, data infrastructure, and domain science. The candidate will have established expertise across automated and autonomous experimental platforms and AI in addition to leadership of multi-disciplinary research programs and the development of novel research concepts.

The scientist will lead Argonne's Rapid Prototyping Laboratory (RPL), a team of computer scientists, roboticists, data scientists, and subject matter experts, who develop hardware and software infrastructure for laboratory autonomy, support autonomous laboratories in domains including chemistry, biology, and quantum science, work with domain scientists to execute autonomous experiments, and advance laboratory autonomy and robotics.

RPL develops the open-source Modular Autonomous Discovery for Science (MADSci) software framework for the orchestration of autonomous laboratories in addition to software infrastructure supporting the operation, training, and execution of robotic workflows. The scientist would be responsible for directing activities towards the advancement of these internal capabilities in addition to the support and development of collaborations across Argonne and beyond.

Focus Areas (expertise in one or more is highly desirable):

  • Autonomous laboratories for chemistry, materials, biology, etc.
  • AI/ML for predictive modeling and inverse design
  • Generative models, reinforcement learning, and agent-based approaches to streamline experimentation and accelerate discovery
  • Integration of HPC, data infrastructure, and ML pipelines for data-driven and autonomous research
  • Digital twins and simulation-augmented AI tools

Key Responsibilities:

  • Guide the development of infrastructure for laboratory autonomy including physical autonomous laboratories, robotics laboratories, and software frameworks for autonomous science and robotics
  • Facilitate collaborations between the RPL and domain scientists across Argonne and partner institutions in the execution of successful autonomous science demonstrations
  • Facilitate collaborations between the RPL and teams at partner institutions developing autonomous science and robotics infrastructure
  • Guide the RPL team towards the advancement of laboratory autonomy and robotics
  • Publish in refereed journals and present at conferences, symposia, and seminars
  • Provide work direction and mentorship to postdoctoral appointees, research assistants, students, and technical staff
  • Execute all activities in compliance with Argonne's safety policies, Safeguards and Security policies, work rules, and safe practices

Position Requirements

  • Completed Ph.D. in Computer Science, Materials Science, Physics, Chemistry, or a related field, and a minimum of 4+ years of related experience
  • Proven research track record in deploying automated and autonomous platforms and AI/ML towards accelerating science
  • Demonstrated ability to formulate scientific problems relevant to the DOE portfolio
  • Strong oral and written communication skills, with the ability to work effectively with internal and external collaborators to achieve established goals
  • Demonstrated ability to collaborate in a multidisciplinary environment and provide scientific guidance to a diverse research community
  • Ability to model Argonne's core values of impact, safety, respect, integrity, and teamwork

Application Instructions:

Submit the following materials as attachments to your application:

  • Cover letter detailing how your experience and expertise align with and will contribute to this position
  • Curriculum vitae with publication list
  • 1-page research statement outlining proposed research directions

About Argonne and the Rapid Prototyping Lab


Argonne National Laboratory is a U.S. Department of Energy multidisciplinary science and engineering research center, operated by UChicago Argonne, LLC. Argonne tackles the largest scientific and engineering challenges of our time, from clean energy and advanced materials to artificial intelligence and quantum information science.

The Rapid Prototyping Lab (RPL), in the Data Science and Learning division, develops integrated hardware and software solutions to accelerate scientific discovery through robotics and AI. RPL serves as a software and robotics hub where scientists collaborate, train the next-generation autonomous-discovery workforce, and develop open-source infrastructure for self-driving labs. RPL projects span new materials for energy storage, discovery of antimicrobial compounds, isotope production for medical applications, and more.

MADSci is RPL's flagship open-source software ecosystem and a core enabling technology for Argonne's broader Autonomous Discovery initiative, which aims to transform laboratory science by combining robotics, AI, and simulation to design, execute, and learn from experiments at unprecedented scale.

For more information:

  • Rapid Prototyping Lab:https://rpl.cels.anl.gov/
  • Autonomous Discovery at Argonne:https://www.anl.gov/autonomous-discovery
  • MADSci on GitHub:https://github.com/AD-SDL/MADSci
  • AD-SDL organization on GitHub:https://github.com/AD-SDL

Job Family

Research Development (RD)

Job Profile

Computational Science 3

Worker Type

Regular

Time Type

Full timeThe expected hiring range for this position is $116,250.00 - $181,350.00.

Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and responsibilities of the position, the qualifications of the selected candidate, business considerations, internal equity, and external market pay for comparable jobs. Additionally, comprehensive benefits are part of the total rewards package.

Click here to view Argonne employee benefits!

As an equal employment opportunity employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation. Argonne encourages everyone to apply for employment. Argonne is committed to nondiscrimination and considers all qualified applicants for employment without regard to any characteristic protected by law.

Argonne employees, and certain guest researchers and contractors, are subject to particular restrictions related to participation in Foreign Government Sponsored or Affiliated Activities, as defined and detailed in United States Department of Energy Order 486.1A. You will be asked to disclose any such participation in the application phase for review by Argonne's Legal Department.

All Argonne offers of employment are contingent upon a background check that includes an assessment of criminal conviction history conducted on an individualized and case-by-case basis. Please be advised that Argonne positions require upon hire (or may require in the future) for the individual be to obtain a government access authorization that involves additional background check requirements. Failure to obtain or maintain such government access authorization could result in the withdrawal of a job offer or future termination of employment.

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