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Campus AI Research Engineer – Deep Learning (Full-Time)

Jump Trading · Chicago, IL · Front Office

Posted Aug 18, 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 research project you took from a rough idea all the way to something running in production. What did that path actually look like.

whether the candidate has real experience owning the full research-to-production lifecycle, not just the modeling part

Describe a time you had to explain a modeling decision to someone without a machine learning background, like a trader. How did you bridge that gap.

ability to communicate technical tradeoffs to non-ML domain experts, since this role sits between researchers and quants

Tell me about a research idea of yours that didn't pan out. What made you decide to stop, and what did you take away from it.

intellectual honesty and judgment about when to cut losses on a research direction

What's a strong opinion you hold about how ML research or infrastructure should be done, that you've had to defend to other engineers.

whether the candidate has developed real convictions from experience, matching the posting's call for 'strong opinions on best practices'

Technical

Tell me about a paper or project of yours that used transformers or state space models. What was the core idea, and what did you have to do differently from the standard recipe.

depth of understanding of modern architectures versus surface familiarity with buzzwords

You need to squeeze extra performance out of a CUDA kernel that's become a bottleneck. What's your process for figuring out whether it's compute-bound, memory-bound, or something else.

actual low-level GPU optimization experience versus theoretical knowledge

Tell me about a model you built where latency mattered a lot at inference time. What tradeoffs did you make between accuracy and speed.

real experience deploying ML where production constraints, not just offline metrics, drove design choices

Walk me through a distributed training run you've done. How many nodes, what framework, and what actually broke or slowed things down.

genuine hands-on scale, since this is a common area where people inflate their experience

Tell me about the most complicated C++ or Python codebase you've worked in. What made it hard to extend, and what would you have done differently.

engineering pragmatism and code quality instincts beyond pure research scripting

How do you decide, on a new open-ended problem, whether to reach for a well-established architecture or try something novel.

research judgment and pragmatism balancing state-of-the-art ambition against delivery timelines

Situational

Say you have a training pipeline that's badly underusing your GPU cluster. How would you go about finding where the time is actually going.

practical profiling instincts and comfort diagnosing HPC bottlenecks rather than guessing

If you were handed a system where researchers complain that iteration cycles are too slow, what would you look at first, and what would you change.

whether the candidate thinks about research velocity as a systems problem, not just model quality

Suppose you're building a framework meant to be reused across multiple research teams working on different trading problems. How do you decide what to make general versus what to leave flexible for each team to customize.

ability to design reusable infrastructure without over-engineering, a core part of the stated job

If a model is performing well on your offline validation set but you're not confident it will hold up once deployed against live, adversarial market data, what would make you trust it or not trust it.

market intuition and awareness of the difference between static ML benchmarks and live trading environments

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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. Our trading teams are each comprised of a dynamic group of traders, quantitative researchers, and engineers who work together to examine the global markets, seeking to understand the complexities of various traded products and exchanges. They leverage their impeccable statistical analysis and data mining skills, using the results of their research to make forecasts and develop profitable predictive trading models. We are seeking research scientists with a demonstrated ability to apply machine 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 AI/ML expertise, engineering pragmatism, statistics, and market intuition.    What You'll Do:  <ul> <li data-leveltext="" data-font="Symbol" data-listid="5" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" data-aria-posinset="1" data-aria-level="1">Apply state-of-the-art techniques to complex and challenging domains. </li> </ul> <ul> <li data-leveltext="" data-font="Symbol" data-listid="5" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" data-aria-posinset="2" data-aria-level="1">Work closely with researchers and quants to build flexible and reusable frameworks for financial ML. </li> </ul> <ul> <li data-leveltext="" data-font="Symbol" data-listid="5" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" data-aria-posinset="3" data-aria-level="1">Optimize training pipelines to make the best use of our HPC resources. </li> </ul> <ul> <li data-leveltext="" data-font="Symbol" data-listid="5" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" data-aria-posinset="4" data-aria-level="1">Integrate ML models into production systems where latency matters. </li> </ul> <ul> <li data-leveltext="" data-font="Symbol" data-listid="5" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" data-aria-posinset="5" data-aria-level="1">Work across a mix of programming languages: C / C++ / Python / CUDA and other low-level GPU languages. </li> </ul> <ul> <li data-leveltext="" data-font="Symbol" data-listid="5" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" data-aria-posinset="6" data-aria-level="1">Build large-scale ML systems that are observable, performant, and flexible. Help improve productivity by reducing the iteration cycle time on research. </li> </ul> <ul> <li data-leveltext="" data-font="Symbol" data-listid="5" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" data-aria-posinset="7" data-aria-level="1">Other duties as assigned or needed. </li> </ul>   Skills You'll Need:  <ul> <li data-leveltext="" data-font="Symbol" data-listid="6" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" data-aria-posinset="1" data-aria-level="1">Strong publication record at ICML, ICLR, AAAI, NeurIPS, UAI, KDD, or equivalent and/or contributions to open-source AI research </li> </ul> <ul> <li data-leveltext="" data-font="Symbol" data-listid="6" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" data-aria-posinset="2" data-aria-level="1">Strong general ML background with exposure to modern deep learning techniques and/or language modeling architectures (e.g. transformers, SSMs) </li> </ul> <ul> <li data-leveltext="" data-font="Symbol" data-listid="6" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" data-aria-posinset="3" data-aria-level="1">Solid development skills in Python and/or C++ </li> </ul> <ul> <li data-leveltext="" data-font="Symbol" data-listid="6" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" data-aria-posinset="4" data-aria-level="1">Familiarity with ML libraries/frameworks such as PyTorch, JAX, and/or TensorFlow </li> </ul> <ul> <li data-leveltext="" data-font="Symbol" data-listid="6" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" data-aria-posinset="5" data-aria-level="1">Intellectual curiosity, versatility, and originality combined with a pragmatic outlook </li> </ul> <ul> <li data-leveltext="" data-font="Symbol" data-listid="6" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" data-aria-posinset="6" data-aria-level="1">Ability to thrive in a collaborative, team-oriented environment </li> </ul> <ul> <li data-leveltext="" data-font="Symbol" data-listid="6" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" data-aria-posinset="7" data-aria-level="1">Ability to reason through quantitative problems and communicate effectively with trading researchers </li> </ul> <ul> <li data-leveltext="" data-font="Symbol" data-listid="6" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" data-aria-posinset="8" data-aria-level="1">Reliable and predictable availability </li> </ul>   Bonus Points:  <ul> <li data-leveltext="" data-font="Symbol" data-listid="7" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" data-aria-posinset="1" data-aria-level="1">Experience with HPC and distributed large model training </li> </ul> <ul> <li data-leveltext="" data-font="Symbol" data-listid="7" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" data-aria-posinset="2" data-aria-level="1">Experience with GPU performance optimization (CUDA or ROCm) </li> </ul> <ul> <li data-leveltext="" data-font="Symbol" data-listid="7" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" data-aria-posinset="3" data-aria-level="1">Experience with end-to-end model development </li> </ul> <ul> <li data-leveltext="" data-font="Symbol" data-listid="7" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" data-aria-posinset="4" data-aria-level="1">Strong opinions on best practices in ML research, tooling, and/or infrastructure </li> </ul>   INTERNATIONAL STUDENTS are encouraged to apply. We accept students eligible for CPT/OPT and we sponsor work visas for full-time positions.  The estimated base salary for this role is $300,000 per year. <hr> Benefits    - Discretionary bonus eligibility    - Medical, dental, and vision insurance    - HSA, FSA, and Dependent Care options    - Employer Paid Group Term Life and AD&D Insurance    - Voluntary Life & AD&D insurance    - Paid vacation plus paid holidays    - Retirement plan with employer match    - Paid parental leave    - Wellness Programs

Apply at Jump Trading

Campus AI Research Engineer – Deep Learning (Full-Time)

Likely interview questions for this role

Practice this interview out loud.

The full job description

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