Jobs › Jump Trading

Research Scientist/Research Engineer, Reinforcement Learning

Jump Trading · Chicago, IL · Front Office

Posted Aug 26, 2026 · We last checked this listing on Sep 20, 2026

Apply at Jump Trading

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 an RL system you built that made it into production, from the original research idea to the final deployed policy.

whether the candidate has real experience crossing the research-to-production gap, not just publishing papers

Tell me about a time your research direction failed after a lot of invested effort. What did you do next?

resilience and intellectual honesty when results don't validate the hypothesis

Describe a moment where you had to explain a technical RL result to a trading audience with no ML background. How did you approach it?

ability to translate research findings for non-technical, outcome-focused stakeholders

Tell me about a disagreement you had with a collaborator over research direction or methodology. How did it get resolved?

collaboration and conflict-handling in a team-oriented, competitive research environment

Technical

How would you design a reward formulation for a trading policy when the true objective, like long-term PnL, is sparse and delayed compared to the signals you get moment to moment?

depth of understanding of reward shaping and horizon tradeoffs specific to financial decision-making

What choices would you make in modeling market microstructure, fill dynamics, and latency inside a simulator so that it stays faithful to live trading?

whether the candidate has hands-on experience building or fixing high-fidelity trading simulators, not just RL theory

How do you evaluate whether an RL policy is actually good out-of-sample, versus just overfit to backtest data or a particular market regime?

rigor in evaluation methodology and awareness of overfitting risks unique to noisy, non-stationary financial data

What policy architectures have you used for sequential decision problems, and how did you decide between them for a given problem?

breadth and judgment in architecture selection, not just familiarity with a checklist of algorithms

If you had to build tooling to store and process very large volumes of market and signal data for training, what would you prioritize first and why?

engineering judgment and prioritization skills when infrastructure work competes with research time

What's an example of an objective horizon choice you made, short-term versus long-term reward, and what tradeoffs did that involve?

nuanced grasp of horizon selection and its practical consequences on policy behavior

Situational

Say your simulator shows a policy outperforming, but live paper trading shows it underperforming. How do you go about figuring out where the mismatch is coming from?

practical debugging instinct around sim-to-real gaps, specifically simulation fidelity issues like fills, latency, and liquidity

A trading team hands you a new alpha signal and wants it integrated into your RL framework quickly. Walk me through how you'd validate it before trusting the policy to act on it.

process discipline when integrating external signals under time pressure, and collaboration style with non-research stakeholders

Suppose a deployed policy starts behaving unexpectedly in live markets during a volatile period. What is your immediate plan of action?

judgment under pressure and awareness of the real financial stakes of live RL systems

Practice this interview out loud.

Offer builds a real interview for this exact role at Jump Trading from your resume and this job description, asks the questions one at a time, and tells you what landed. The first one is free.

Practice this out loud

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 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 <ul> <li>5+ years of experience developing reinforcement learning and/or deep learning systems with measurable impact in industry and/or academia</li> <li>Depth in reinforcement learning, including experience designing reward formulations, policy architectures, and evaluation, and taking RL methods from research into production</li> <li>Proficiency in Python and/or C++</li> <li data-renderer-start-pos="1258">Familiarity with ML libraries/frameworks such as PyTorch (preferred), TensorFlow, and/or JAX</li> <li>Strong foundation in mathematics and statistics</li> <li>PhD or Master's degree in Computer Science, Machine Learning, Robotics (or a related subject)</li> <li>Strong publication record at ICML, ICLR, AAAI, NeurIPS, CVPR, or equivalent</li> <li>Ability to thrive in a collaborative, team-oriented environment</li> <li>Creative thinkers who are driven, self-motivated, and eager to solve challenging problems</li> <li>Reliable and predictable availability</li> <li class="font-claude-response-body whitespace-normal break-words pl-2">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>

Apply at Jump Trading

Research Scientist/Research Engineer, Reinforcement Learning

Likely interview questions for this role

Practice this interview out loud.

The full job description

Related jobs

Technical Project Manager

Network Engineer - Wireless

Data Center Technician

Senior Accounting Analyst | Corporate Accounting

HPC Data Center Infrastructure Planning Lead

Digital Modem Engineer