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Postdoctoral Fellow Machine Learning Jobs in Oak Brook, IL

EVOZYNE INC

Applied AI Research Fellow

Chicago, IL · On-site

Late-stage PhD student or postdoc in a quantitative or computational field * Hands-on experience ... Familiarity with modern machine learning approaches (e.g., deep learning, generative models, or ...

Vizient

Data Science Fellow

Chicago, IL

$51K - $87K/yr

Develop data science approaches, including machine learning models, improve understanding and ... A one-page personal statement (explaining why you are interested in Vizient and this fellowship)

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Postdoctoral Fellow Machine Learning information

See Oak Brook, IL salary details

$25.2K

$59.6K

$84.3K

How much do postdoctoral fellow machine learning jobs pay per year?

As of Aug 16, 2026, the average yearly pay for postdoctoral fellow machine learning in Oak Brook, IL is $59,566.00, according to ZipRecruiter salary data. Most workers in this role earn between $49,500.00 and $67,100.00 per year, depending on experience, location, and employer.

What is a postdoctoral fellow in machine learning?

A Postdoctoral Fellow in Machine Learning is a researcher who has recently completed their PhD and is engaged in advanced research in the field of machine learning. This role typically involves conducting independent or collaborative research, publishing scientific papers, and sometimes mentoring students. Postdoctoral fellows often work at universities, research institutes, or industry labs, focusing on developing new algorithms, improving existing models, or applying machine learning techniques to specific problems. The position is usually temporary, lasting one to three years, and aims to prepare researchers for permanent academic or industry roles.

What is the difference between Postdoctoral Fellow Machine Learning vs Postdoctoral Research Scientist?

AspectPostdoctoral Fellow Machine LearningPostdoctoral Research Scientist
Required credentialsPhD in Computer Science, Data Science, or related fieldPhD in relevant field, often with specialized research experience
Work environmentAcademic labs, universities, research institutionsResearch labs, industry R&D departments, tech companies
Employer and industry usagePrimarily academia, government researchPrimarily industry, corporate research divisions
Common search and comparison intentUnderstanding academic research roles in machine learningExploring industry-focused research career paths

Postdoctoral Fellow Machine Learning roles typically focus on academic research, requiring a PhD and working in universities or research institutions. In contrast, Postdoctoral Research Scientist positions are often industry-based, emphasizing applied research within corporate R&D departments. Both roles involve advanced machine learning expertise but differ mainly in work environment and career trajectory.

What are the key skills and qualifications needed to thrive as a postdoctoral fellow in machine learning?

To thrive as a Postdoctoral Fellow in Machine Learning, you need a strong background in computer science, mathematics, and statistics, typically supported by a PhD and relevant research experience. Familiarity with programming languages such as Python, machine learning frameworks like TensorFlow or PyTorch, and experience in high-performance computing environments are commonly required. Strong analytical thinking, effective scientific communication, and collaboration skills help you contribute to research teams and disseminate findings. These skills and qualities are crucial for advancing research, developing innovative solutions, and building a successful academic or industry career in machine learning.

What are some common challenges faced by postdoctoral fellows in machine learning, and how can they be addressed?

Postdoctoral Fellows in Machine Learning often encounter challenges such as balancing independent research with collaborative projects, staying current with rapidly evolving technologies, and securing funding or publishing in top-tier journals. To address these, it's helpful to establish clear communication with mentors and collaborators, set aside dedicated time for reading recent literature, and actively seek feedback on research drafts. Building a professional network through conferences and seminars can also open opportunities for collaboration and career advancement.

What cities near Oak Brook, IL are hiring for Postdoctoral Fellow Machine Learning jobs?

Cities near Oak Brook, IL with the most Postdoctoral Fellow Machine Learning job openings:

Postdoctoral Fellow Machine Learning jobs near you
Infographic showing various Postdoctoral Fellow Machine Learning job openings in Oak Brook, IL as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 21% Part Time, 1% Temporary, and 2% Contract. Highlights an 89% Physical, 2% Hybrid, and 9% Remote job distribution, with an average salary of $59,566 per year, or $28.6 per hour.

Applied AI Research Fellow

EVOZYNE INC

Chicago, IL • On-site

Full-time

Re-posted 18 days ago

Job description

Evozyne is one of the few AI-native biotech companies designing de novo therapeutic proteins and advancing them toward the clinic. Our teams apply AI to develop novel therapies within complex biological systems where data is imperfect, and discoveries have meaningful impact on patients’ lives. We are transforming how the industry approaches protein engineering.

Our platform, EvoGen, is both generative and predictive. Rather than focusing on structure alone, we build models that learn how protein sequence drives function, enabling the design of novel proteins optimized across multiple objectives, including potency, stability, specificity, and immunogenicity. Our model development is tightly integrated with proprietary experimental data, enabling rapid learning from biological reality. We combine sequence-based models and variational autoencoders (VAEs) with Bayesian optimization, using experimental data to rapidly design and refine proteins into impactful therapeutics.

The Applied AI Research Fellowship at Evozyne is designed for researchers who want to stress‑test ambitious ideas against one of the most challenging frontiers in applied AI today: generative design under real‑world biological constraints. As a Fellow, you will work on foundational questions in representation learning, generative modeling, and optimization, with the opportunity to see your ideas evaluated against real experimental outcomes and translated into therapeutic programs.

Your work will directly influence how Evozyne evaluates, evolves, and deploys its generative AI models for protein design.

Who You Are

You’re excited by problems where the data is messy, the constraints are real, and the path forward isn’t obvious. You thrive in ambiguity, and you’re motivated by applying your work to real-world scientific challenges to see how your ideas hold up in practice. You are already operating at the leading edge of applied AI and want to push your thinking further by applying it to complex, high-impact challenges in drug discovery.

What You’ll Be Investigating

As an Applied AI Research Fellow, you will help drive the evolution of Evozyne’s generative AI design platform. Example research areas include:

  • Benchmarking generative protein models, including Evozyne’s own, on their ability to produce functionally diverse and biologically meaningful designs.
  • Evaluating the value of integrating large-scale metagenomic resources (e.g., Global Ocean Gene Catalog) into current internal database.
  • Exploring alternatives and extensions to Bayesian Optimization such as Knowledge Gradient, Entropy Search, and related methods for multi-objective optimization problems.
  • Developing and applying deep learning approaches for remote homology detection
  • Investigating multi-family VAE models to enable protein design when sequence support is limited or when optimizing phenotypes across protein families.

These efforts are intended to surface failure modes, challenge assumptions, and directly inform how Evozyne designs proteins and advances therapies.

Education + Experience

  • Late-stage PhD student or postdoc in a quantitative or computational field
  • Hands-on experience applying AI/ML to complex, real-world or scientific datasets
  • Experience working on problems where data is noisy, incomplete, or difficult to interpret
  • Familiarity with modern machine learning approaches (e.g., deep learning, generative models, or related methods)
  • Evidence of meaningful contribution to research, open-source work, or applied projects
  • Exposure to interdisciplinary work (e.g., biology, chemistry, physics, or other scientific domains) is a plus

Why Evozyne

Few places offer the combination of proprietary experimental data, real therapeutic programs, and the freedom to explore foundational AI questions under real biological constraints. If you want your best ideas tested where they matter most, and the chance to help redefine how AI is applied to protein design, we’d like to connect.