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Machine Learning Researcher

IMC Trading · Chicago, IL · Trading

Posted Sep 30, 2026 · We last checked this listing on Oct 6, 2026

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Likely interview questions for this role

Written from this job description, not a generic list. Each one notes what the interviewer is really checking.

About your past

Walk me through a feature you engineered that made a real difference, and how you knew it wasn't just overfitting.

What they are checking: Hands-on feature engineering skill and the discipline to prove a signal is real.

Tell me about a time you worked with engineers to get a model from research into production.

What they are checking: Experience closing the gap between research code and reliable systems, which this central team needs.

Describe a research idea you abandoned. How did you decide to stop?

What they are checking: Judgment about sunk cost and the ability to kill weak ideas quickly.

What would you do

A trader tells you your model's signal makes no sense to them. How do you respond?

What they are checking: Whether they value trading intuition and can explain or test a model rather than defending it blindly.

If your model's live performance drops after a month, what are the first things you check?

What they are checking: Systematic monitoring habits, separating data pipeline bugs from real signal decay.

About the work

Financial data is noisy and non-stationary. How does that change the way you validate a model compared to image or text work?

What they are checking: Whether they know about walk-forward validation, regime shifts and why random splits leak the future.

You get a 0.02 improvement in out-of-sample correlation. How do you decide if that's worth deploying?

What they are checking: Whether they can link a small statistical gain to trading value, costs and risk.

How would you use unstructured data, like news text, as an input to a market prediction model?

What they are checking: Practical sense for turning messy data into features and the timing traps around when data was actually known.

When would you choose a gradient boosted tree over a deep network for a trading signal?

What they are checking: Pragmatism about model choice rather than reaching for the newest architecture by default.

How would you detect look-ahead bias in a dataset someone else built?

What they are checking: Rigor about data timing, which is one of the most common ways trading research quietly fails.

How would you approach building a custom model for a problem where off-the-shelf architectures did poorly?

What they are checking: Research depth and whether they can reason from the problem structure to model design.

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The full job description

As published by IMC Trading.

IMC Trading is seeking quantitative researchers with a proven track record to apply state-of-the-art machine learning & deep learning to solve challenging trading problems. This role is part of a central ML research team that collaborates across trading teams at IMC.  The ideal candidate will have experience working with other researchers and engineers to build and continuously improve models, systems, and research tooling. We firmly believe that success for research-driven efforts lies in bringing together skills in ML, statistics, and trading intuition as well as a problem-solving mindset and pragmatism.  This is an opportunity to dive deep into feature engineering and alpha research, and focus on applying a wide range of ML models as well as to perform research on building custom models. Your Core Responsibilities: • Design and deploy machine learning models to enhance trading performance across various asset classes • Research, test and prototype new algorithmic ideas; deploy advanced ML techniques applicable to market prediction, signal generation, and portfolio optimization • Collaborate with quantitative traders, researchers, and developers to translate market insights into data-driven features and models • Manage data acquisition, preprocessing, and feature engineering for structured and unstructured data sources Your Skills and Experience: • PhD or Master’s in Engineering, Math, Statistics, Computer Science, or related quantitative field • 2+ years of experience building applied ML models; previous experience in trading environment preferred • Proven expertise in developing and deploying predictive models • Strong programming skills in Python; proficiency in ML libraries such as PyTorch, TensorFlow, and/or high-performance libraries like Jax • Strong understanding of theoretical foundations of state-of-the-art ML models • Strong publication track record at ICML, ICLR, NeurIPS, or equivalent • Ability and desire to work in a collaborative team environment • Excellent written and verbal communication skills The Base Salary range for the role is included below. Base salary is only one component of total compensation; all full-time, permanent positions are eligible for a discretionary bonus and benefits, including paid leave and insurance. Please visit Benefits - US | IMC Trading for more comprehensive information. Salary Range $250,000 — $300,000 USD About Us IMC is a research-driven trading firm where quantitative modeling, machine learning, and engineering shape how modern markets are traded. A stabilizing force in markets since 1989, we provide liquidity across trading venues, delivering the best outcome in value and risk management to investors. Using our own technology and capital, we build proprietary systems and algorithms that operate across global markets. Our researchers, traders, and engineers work as a collective, combining rapid experimentation, advanced infrastructure, and real-time feedback to turn insight into execution and execution into advantage.  

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