Jobs › Jump Trading

Research Engineer, Pre-Training

Jump Trading · Chicago, IL · Front Office

Posted Aug 14, 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

Tell me about a time your model training job failed partway through on a large GPU cluster. What did you do?

real experience with fault tolerance, checkpointing, and staying calm under production pressure

Tell me about a research idea you had that didn't pan out at scale even though it looked promising in small experiments.

honesty about failure and understanding of how scaling laws can betray small-scale intuition

Tell me about a disagreement you had with a researcher over an architecture choice where you were the one focused on engineering constraints.

ability to balance ambitious research goals against practical limits, and how they handle friction with researchers

Technical

Walk me through a large-scale training run you owned end to end. Where was the bottleneck, and how did you find it?

whether the candidate has actually operated at scale versus just read papers about it

How do you think about MFU and when is it the wrong metric to optimize?

depth of understanding versus reciting a buzzword they've heard in interviews

Describe a custom kernel you wrote or optimized. What made the stock implementation insufficient?

hands-on low-level engineering skill, not just framework-level API usage

How would you design a data pipeline that needs to stream terabytes per second without starving the GPUs?

practical systems design sense for I/O-bound training at scale

Explain a paper of yours or a method you contributed to on efficient training or scaling laws, and what the actual novel contribution was versus incremental tuning.

whether their publication record reflects real insight or was a co-author credit

You're given a choice between PyTorch and JAX for a new pre-training effort. How do you decide, and what are you giving up either way?

depth of framework knowledge and ability to reason about tradeoffs rather than brand loyalty

What's the most subtle numerical precision bug you've encountered in mixed-precision training, and how did you track it down?

depth of hands-on debugging experience with mixed precision, not textbook knowledge

Situational

Say you're training a model across thousands of GPUs and TPUs and you're getting sub-linear scaling. What's your process for isolating whether it's networking, kernel efficiency, or data pipeline?

systematic debugging approach across the full stack rather than guessing

If you had a fixed compute budget and had to choose between a bigger model or more tokens, how would you reason through it, and what would change your answer here specifically for financial market data?

grasp of scaling laws and whether they can adapt general ML knowledge to the trading-data context

How do you approach a week where your training runs are producing results that directly affect live trading decisions and something looks off?

judgment under pressure when research work has immediate financial consequences

What does reliable, predictable availability mean to you in a role where infrastructure failures can happen at any hour?

whether they understand and accept the operational demands of running production-critical training infrastructure

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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 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 foundation model research and trading at Jump. Our mission is to combine emerging techniques and original research to generate signals from financial market data and monetize it globally. We are building the future of ML-powered trading through breakthrough foundation models, and we're looking for an exceptional Pre-Training Engineer to join our team. What You'll Do: As a Pre-Training Research Engineer, you'll be at the forefront of developing massive-scale foundation models that fundamentally transform how we understand and predict markets. You'll own and drive the entire training stack: building fault-tolerant infrastructure that scales across thousands of GPUs and TPUs with near-linear performance, engineering data pipelines that stream terabytes per second as our models train on petabytes of data from every corner of the global markets, and designing custom kernels that unlock 10x efficiency gains. Co-designing novel architectures with researchers and pioneering cutting-edge approaches to mixed-precision training and model parallelism, you'll have the latest generation hardware at your disposal. This isn't incremental optimization; we're pushing the boundaries of what's possible in pre-training at scale, where your improvements directly impact live trading. Other duties as assigned or needed. Skills You'll Need: <ul> <li>Expertise and track record of significant, measurable performance improvements in large-scale distributed training (MFU, throughput, convergence, cost-per-token).</li> <li>Published research in efficient training methods, scaling laws, architectures, or systems for ML</li> <li>Background in numerical computing, HPC, or distributed systems, including familiarity with GPUs/TPUs, high-performance networking (NVLink/InfiniBand), Kubernetes/Slurm, and OS internals</li> <li>Expertise in Python and deep experience with modern deep learning frameworks (PyTorch and/or JAX)</li> <li>Advanced degree (MS or PhD) in Computer Science, Machine Learning, Physics, Mathematics, or a related quantitative field, or equivalent industry experience at a frontier lab</li> <li>Ability to balance ambitious research goals with practical engineering constraints</li> <li>Strong problem-solving skills, results orientation, and excellent collaborative communication</li> <li>Reliable and predictable availability</li> </ul> Bonus Points: <ul> <li>Expertise in: CUDA kernel development, Triton/Pallas/CuTe DSLs, PyTorch/JAX internals, XLA optimization, or hardware acceleration (FPGA/ASIC)</li> <li>Knowledge of reinforcement learning, post-training, or fine-tuning techniques</li> <li>Knowledge of financial markets or trading</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">$300,000—$350,000 USD</div></div></div>

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Research Engineer, Pre-Training

Likely interview questions for this role

Practice this interview out loud.

The full job description

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