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Research Scientist/Research Engineer | Deep Learning

Jump Trading · Chicago, IL · Front Office

Posted Aug 8, 2026 · We last checked this listing on Sep 20, 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.

Behavioral

Walk me through a deep learning project you took from a raw idea all the way to something running in production. What broke along the way?

whether the candidate can own the full research-to-deployment lifecycle, not just publish a paper

Tell me about a time your model looked great on your validation set but failed once it hit real, live data. What did you do?

awareness of distribution shift and overfitting risk, which is central to trading where the future never looks like the backtest

Describe a research paper of yours or a project result you're most proud of, and tell me the one thing you'd redo differently now.

intellectual honesty and depth of understanding versus a rehearsed highlight reel

Tell me about a disagreement you had with a teammate or advisor over model design or an experimental result. How did it get resolved?

ability to collaborate and hold a position with intellectual honesty in a high-friction, competitive research culture

Tell me about a time you had to explain a complicated modeling result to someone who wasn't technical, like a trader or a portfolio manager. How did you adjust?

communication skill needed to translate research into decisions that traders will actually act on

Technical

How would you design an architecture to model a noisy, non-stationary time series like an order book, where the signal-to-noise ratio is very low?

depth of applied deep learning knowledge specific to financial time series rather than textbook vision or NLP setups

Explain the bias-variance tradeoff to me as if I were a smart engineer who's never trained a neural net.

whether foundational statistics are genuinely internalized, not just cited

When would you reach for a transformer versus a simpler recurrent or convolutional architecture, and what would make you decide it's not worth the added complexity?

engineering pragmatism, since the posting explicitly wants people who balance sophistication against real cost

How do you profile and speed up a slow training pipeline in Python or C++ when GPU hours are limited and everyone on the team wants them?

engineering skill and resourcefulness under shared infrastructure constraints

Situational

Say you have a deep learning model that shows a strong backtested edge but the signal decays fast after a few weeks live. How would you investigate whether it's noise, overcrowding, or a structural break in the market?

market intuition combined with statistical rigor, not just ML mechanics

If I gave you a dataset with strong lookahead bias baked in by mistake, how would you go about finding it before it costs the desk money?

discipline around data hygiene and skepticism, since silent leakage is the classic way quant models fail

You're given an open-ended mandate to improve a piece of the monetization pipeline with no clear starting point. How do you decide what to work on first?

comfort with ambiguity and self-direction, since the role is explicitly open-ended rather than assigned tickets

If two of your models disagree on direction for the same signal, how would you decide whether to combine them, pick one, or scrap both?

judgment about ensembling and risk versus chasing marginal gains blindly

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The full job description

As published by Jump Trading.

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 incenting 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. The quantitative trading teams at Jump Trading probe and examine the global markets, seeking to understand the complexities of various traded products and exchanges. They leverage their impeccable statistical analysis, machine learning and deep learning skills, using the results of their research to make forecasts and develop profitable predictive trading models. What You’ll Do:  We are seeking research scientists with a demonstrated ability to apply deep learning to achieve state-of-the-art capabilities in complex and challenging domains. The ideal person for this role will be capable of implementing an open-ended research project from concept to production and continuously improving model design, tools, and infrastructure. Potential projects may target any area of the quantitative research and monetization process. We believe that successful research efforts require a fluid mix of skills including ML expertise, engineering pragmatism, statistics and market intuition. Other duties as assigned or needed. Skills You’ll Need: <ul> <li>5+ years of experience in developing DL systems with measurable impact in industry and/or academia</li> <li>Creative thinkers who are driven, self-motivated, and eager to solve challenging problems</li> <li>Proficiency in Python and/or C++</li> <li>Strong foundation in mathematics and statistics</li> <li>Ability to thrive in a collaborative, team-oriented environment</li> <li>PhD, or Master's degree in Computer Science, Mathematics, (or related subject)</li> <li>Strong publications record at ICML, ICLR, AAAI, NeurIPS, UAI, KDD, or equivalent</li> <li>Reliable and predictable availability</li> <li>Excellent written and verbal communication skills in English</li> </ul>  <div class="content-pay-transparency"><div class="pay-input"><div class="description">Benefits <ul> <li style="font-size: 14px;">Discretionary bonus eligibility </li> <li style="font-size: 14px;">Medical, dental, and vision insurance</li> <li style="font-size: 14px;">HSA, FSA, and Dependent Care options</li> <li style="font-size: 14px;">Employer Paid Group Term Life and AD&D Insurance</li> <li style="font-size: 14px;">Voluntary Life & AD&D insurance</li> <li style="font-size: 14px;">Paid vacation plus paid holidays</li> <li style="font-size: 14px;">Retirement plan with employer match</li> <li style="font-size: 14px;">Paid parental leave</li> <li style="font-size: 14px;">Wellness Programs </li> </ul></div><div class="title">Annual Base Salary Range </div><div class="pay-range">$200,000—$350,000 USD</div></div></div>

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Research Scientist/Research Engineer | Deep Learning

Likely interview questions for this role

Practice this interview out loud.

The full job description

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