ZipRecruiter

Log In

1

Reinforcement Learning Engineer Jobs in Chicago, IL

KenkoTech Futures

AI Scientist

Mundelein, IL · On-site

Experience developing, training or evaluating large deep learning models * Strong programming ... Reinforcement Learning * Large-Scale Distributed Training * Single-Cell Foundation Models Why Join

CDW

Principal Consultant- AWS

Vernon Hills, IL · On-site

Providing Engineering Services for AWS, coordinating engagement strategies, and qualifying ... learning, unsupervised learning, reinforcement learning, deep learning, and natural language ...

CDW

Principal Consultant-AWS

Vernon Hills, IL

Providing Engineering Services for AWS, coordinating engagement strategies, and qualifying ... learning, unsupervised learning, reinforcement learning, deep learning, and natural language ...

Maxwell Bond

Chief Technology Officer

Mundelein, IL · On-site

Advanced experience with learning-based approaches including reinforcement learning, imitation ... Build an engineering culture where people are expected to solve difficult problems rather than work ...

previous page

Showing results 41-60

All JobsReinforcement Learning Engineer JobsReinforcement Learning Engineer Jobs in Chicago, IL

Reinforcement Learning Engineer information

See Chicago, IL salary details

$39.1K

$119.4K

$197.3K

How much do reinforcement learning engineer jobs pay per year?

As of Sep 2, 2026, the average yearly pay for reinforcement learning engineer in Chicago, IL is $119,357.00, according to ZipRecruiter salary data. Most workers in this role earn between $85,500.00 and $156,100.00 per year, depending on experience, location, and employer.

What is a reinforcement learning engineer?

Reinforcement Learning Engineers are specialized professionals who design, develop, and implement algorithms based on reinforcement learning, a type of machine learning where agents learn to make decisions by receiving rewards or penalties. They work on building models that enable machines to learn optimal actions through trial and error in complex environments. Their responsibilities often include developing RL architectures, tuning hyperparameters, running simulations, and applying RL methods to real-world problems like robotics, gaming, or recommendation systems. RL Engineers typically have strong backgrounds in computer science, mathematics, and deep learning, along with experience in programming languages like Python and frameworks such as TensorFlow or PyTorch.

What are some common challenges faced by reinforcement learning engineers when deploying models in real-world environments?

One of the main challenges Reinforcement Learning (RL) Engineers face is bridging the gap between simulation and real-world deployment. Models that perform well in controlled environments may struggle with unpredictable data, safety constraints, or limited feedback in production. Additionally, RL algorithms often require significant computational resources and careful tuning to avoid instability. Collaboration with domain experts and software engineers is essential to address these issues and ensure successful integration of RL solutions into existing systems.

What are the key skills and qualifications needed to thrive as a reinforcement learning engineer, and why are they important?

To thrive as a Reinforcement Learning Engineer, you need a strong background in machine learning, mathematics (especially probability and statistics), and programming languages like Python, often supported by a relevant degree in computer science or engineering. Familiarity with deep learning frameworks (such as TensorFlow or PyTorch), RL libraries (like OpenAI Gym), and cloud computing platforms is typically required. Problem-solving skills, creativity, and effective collaboration help set outstanding engineers apart in this field. These competencies enable the design and deployment of advanced RL solutions that address real-world challenges and drive innovation.

What is the difference between Reinforcement Learning Engineer vs Machine Learning Engineer?

AspectReinforcement Learning EngineerMachine Learning Engineer
CredentialsBachelor's/Master's in CS, AI, or related; experience with RL frameworksBachelor's/Master's in CS, Data Science, or related; experience with ML algorithms
Work EnvironmentResearch labs, AI startups, tech companies focusing on RL applicationsTech companies, data-driven firms, AI departments across industries
Industry UsageSpecialized in RL projects like robotics, game AI, autonomous systemsBroader applications including predictive modeling, NLP, computer vision

Reinforcement Learning Engineers focus on developing algorithms that learn through interactions with environments, often in robotics or gaming. Machine Learning Engineers work on a wider range of models and applications. While both roles require strong programming and math skills, RL Engineers specialize in sequential decision-making, whereas ML Engineers handle diverse data-driven tasks across industries.

What are popular job titles related to Reinforcement Learning Engineer jobs in Chicago, IL?

For Reinforcement Learning Engineer jobs in Chicago, IL, the most frequently searched job titles are:

What job categories do people searching Reinforcement Learning Engineer jobs in Chicago, IL look for?

The top searched job categories for Reinforcement Learning Engineer jobs in Chicago, IL are:

What cities near Chicago, IL are hiring for Reinforcement Learning Engineer jobs?

Cities near Chicago, IL with the most Reinforcement Learning Engineer job openings:

Reinforcement Learning Engineer jobs near you
Infographic showing various Reinforcement Learning Engineer job openings in Chicago, IL as of August 2026, with employment types broken down into 87% Full Time, and 13% Contract. Highlights an 79% In-person, and 21% Remote job distribution, with an average salary of $119,357 per year, or $57.4 per hour.

AI Scientist

KenkoTech Futures

Mundelein, IL • On-site

Other

Posted 18 days ago

Job description

📍 Location: San Francisco / Boston

🧬 About: Frontier AI x Biology | Foundation Models | Therapeutic Discovery

💼 Stage: Well-funded AI Biotechnology Company


PLEASE FOLLOW THE KENKOTECH PAGE AND CONNECT WITH THE JOB POSTER


About the Opportunity


We're partnering with one of the world's leading AI x Biology companies, building frontier foundation models designed to transform therapeutic discovery.


The team is developing large-scale multimodal foundation models across biological data modalities - including single-cell genomics, transcriptomics, DNA, RNA and proteins - to create universal biological representations capable of accelerating target discovery, disease understanding and drug development.


This is a rare opportunity to join an exceptionally strong research organisation working at the intersection of large-scale machine learning and modern biology. You'll collaborate with world-class AI researchers, computational biologists and experimental scientists to develop the next generation of biological foundation models that directly power therapeutic discovery.


The role is highly research-focused, but with a strong emphasis on building models that move beyond publications and have real scientific impact.


Key Responsibilities

  • Develop and train large-scale foundation models across biological modalities including DNA, RNA, proteins and single-cell data
  • Research novel model architectures, representation learning approaches and pre-training strategies for biological data
  • Design and implement large-scale distributed training pipelines for frontier AI models
  • Develop methods for multimodal learning across diverse biological datasets
  • Improve model performance through post-training, evaluation, alignment and fine-tuning techniques
  • Work closely with experimental scientists to translate model outputs into biological insight
  • Design rigorous evaluation frameworks for biological foundation models
  • Contribute to the long-term research direction of the company's AI platform
  • Stay at the forefront of developments across machine learning, foundation models and computational biology


Qualifications

  • PhD in Machine Learning, Computer Science, Computational Biology, Bioinformatics, Statistics, Mathematics, Physics or a related quantitative discipline
  • Outstanding research background in modern machine learning
  • Strong publication record at leading conferences or journals (NeurIPS, ICML, ICLR, Nature, Science, Cell, etc.)
  • Experience developing, training or evaluating large deep learning models
  • Strong programming skills using modern ML frameworks (PyTorch, JAX, etc.)
  • Experience with distributed training or large-scale model development is highly desirable
  • Ability to work across both research and engineering to build production-quality systems
  • Ideal Background


We're particularly interested in researchers with experience in one or more of the following:

  • Foundation Models
  • Representation Learning
  • Large Language Models
  • Multimodal Learning
  • Self-Supervised Learning
  • Generative Modelling
  • Reinforcement Learning
  • Large-Scale Distributed Training
  • Single-Cell Foundation Models


Why Join

  1. Help build some of the world's most advanced foundation models for biology
  2. Work alongside internationally recognised AI researchers and computational biologists
  3. Apply frontier AI to real therapeutic discovery problems
  4. Access enormous proprietary biological datasets and large-scale compute
  5. Research with genuine scientific and clinical impact rather than purely academic objectives
  6. Join one of the best-capitalised and fastest-growing AI x Biology organisations in the world
  7. Opportunity to publish, innovate and help shape the future of AI-driven drug discovery

Apply