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Postdoctoral In Reinforcement Learning Jobs in Lincolnwood, IL

Barrington James

Research Engineer

Mundelein, IL · On-site

... in San Francisco. This is a highly hands-on role focused on building reinforcement learning environments and post-training systems for healthcare AI. You'll work across the full RL lifecycle ...

Lightspeed

AI & Machine Learning Engineer

Northbrook, IL · On-site

... reinforcement learning, imitation learning, or robot learning approaches • Experience optimizing ML models for edge deployment (TensorRT, ONNX, quantization) • Strong Python with experience in C ...

Lightspeed

AI & Machine Learning Engineer

Northbrook, IL · On-site

$100 - $130/hr

... in homebuilding. Position Overview: As an AI & Machine Learning Engineer, you will design, build ... Develop reinforcement learning and imitation learning systems for robot task planning * Build ...

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Postdoctoral In Reinforcement Learning information

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$24.9K

$58.7K

$83K

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

As of Aug 27, 2026, the average yearly pay for postdoctoral in reinforcement learning in Lincolnwood, IL is $58,693.00, according to ZipRecruiter salary data. Most workers in this role earn between $48,700.00 and $66,100.00 per year, depending on experience, location, and employer.

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 cities near Lincolnwood, IL are hiring for Postdoctoral In Reinforcement Learning jobs?

Cities near Lincolnwood, 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 Lincolnwood, IL as of August 2026, with employment types broken down into 1% As Needed, 72% Full Time, 25% Part Time, 1% Contract, and 1% Nights. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $58,693 per year, or $28.2 per hour.

Research Scientist/Research Engineer, Reinforcement Learning

Chicago, IL • On-site

Socket.dev
Network Security • 1 - 10 employees
Learn more about Socket.devexternal

$200 - $350/hr

Other

Medical, Dental, Vision, Life, Retirement, PTO

Posted 5 days ago

Job description

Jump Trading Group is committed to world-class research. We empower exceptional talents in Mathematics, Physics, and Computer Science to seek scientific boundaries, push through them, and apply cutting-edge research to global financial markets. Our culture is unique. Constant innovation requires fearlessness, creativity, intellectual honesty, and a relentless competitive streak. We believe in winning together and unlocking unique individual talent by incentivizing collaboration and mutual respect. At Jump, research outcomes drive more than superior risk-adjusted returns. We design, develop, and deploy technologies that change our world, fund start-ups across industries, and partner with leading global research organizations and universities to solve problems.

Our team is a group of quantitative researchers, engineers, and ML experts leading reinforcement learning research and trading at Jump. Our mission is to combine emerging techniques and original research to learn optimal decision-making policies from financial market data and monetize them globally. We are building the future of ML-powered trading through breakthrough reinforcement learning, and we're looking for an exceptional Research Scientist/Research Engineer to join our team.

What You’ll Do

As a Research Scientist/Research Engineer working on RL, you'll be at the forefront of applying reinforcement learning to markets. You'll conduct original research and own the systems that turn it into production trading: designing and evaluating policy architectures, reward formulations, and objective horizons with rigorous out-of-sample benchmarking; partnering with trading and research teams to source, integrate, and validate their alpha signals within the RL framework; ensuring simulation fidelity against live trading by modeling market microstructure, fill dynamics, liquidity, and latency; building efficient tooling to store, process, and analyze very large volumes of market and signal data; and communicating findings to technical and trading audiences. This isn't incremental optimization; we're pushing the boundaries of what reinforcement learning can do at scale, where your improvements directly impact live trading.

Other duties as assigned or needed.

Skills You’ll Need
  • 5+ years of experience developing reinforcement learning and/or deep learning systems with measurable impact in industry and/or academia
  • Depth in reinforcement learning, including experience designing reward formulations, policy architectures, and evaluation, and taking RL methods from research into production
  • Proficiency in Python and/or C++
  • Familiarity with ML libraries/frameworks such as PyTorch (preferred), TensorFlow, and/or JAX
  • Strong foundation in mathematics and statistics
  • PhD or Master's degree in Computer Science, Machine Learning, Robotics (or a related subject)
  • Strong publication record at ICML, ICLR, AAAI, NeurIPS, CVPR, or equivalent
  • Ability to thrive in a collaborative, team-oriented environment
  • Creative thinkers who are driven, self-motivated, and eager to solve challenging problems
  • Reliable and predictable availability
  • Excellent written and verbal communication skills in English
Benefits
  • Discretionary bonus eligibility
  • Medical, dental, and vision insurance
  • HSA, FSA, and Dependent Care options
  • Employer Paid Group Term Life and AD&D Insurance
  • Voluntary Life & AD&D insurance
  • Paid vacation plus paid holidays
  • Retirement plan with employer match
  • Paid parental leave
  • Wellness Programs

Annual Base Salary Range $200,000 — $350,000 USD

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