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Campus AI Research Engineer - Deep Learning (Intern)

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 where you took an idea from a rough concept all the way to a working, evaluated system. What did you build first, and what did you leave for later?

whether the candidate can drive an open-ended research project end to end rather than only executing a well-defined task

Describe a time you had to squeeze more performance out of code that was already 'working.' What tools did you use to find the actual bottleneck, and were you right about where it was before you measured?

instinct for measurement over guesswork when optimizing performance, a core habit for HPC and low-latency work

Tell me about a time your model or approach didn't work, even though the idea seemed sound on paper. How did you figure out what was actually wrong?

intellectual honesty and debugging discipline when results don't match expectations

What's a strong opinion you hold about how ML research infrastructure or tooling should be built, and what experience led you to it?

whether the candidate has actually built or suffered through research tooling, not just used someone else's

Tell me about a time you disagreed with a collaborator, maybe a trader or a more senior researcher, about the right technical approach. What happened?

ability to hold a technical position and communicate it clearly in a collaborative, high-stakes team without being combative

Technical

Tell me about a paper or architecture you've implemented from scratch, like a transformer or a state space model. What details did the paper leave out that you had to figure out yourself?

depth of hands-on understanding of modern deep learning architectures versus surface-level familiarity

Suppose your model trains fine on a single GPU but you need to scale it across many GPUs on our HPC cluster. What are the main bottlenecks you'd expect, and how would you find out which one is actually hurting you?

practical grasp of distributed training and profiling, not just theoretical knowledge of parallelism strategies

If someone handed you a CUDA kernel that was running slower than expected, how would you go about diagnosing whether the problem is memory bandwidth, occupancy, or something else?

real low-level GPU debugging experience versus having only used high-level frameworks

How would you design a training pipeline so that iteration time stays fast even as the dataset or model gets bigger?

understanding of what makes research infrastructure scalable and observable rather than a one-off script

Walk me through your comfort level moving between Python and C++ or CUDA on the same project. Can you give an example where you had to drop down a level to solve a problem?

actual fluency across the language stack the job requires, not just familiarity with one layer

Situational

You're asked to integrate a trained model into a production system where every microsecond matters. What would you change about how you designed or exported the model compared to a pure research setting?

awareness that research code and low-latency production code have very different constraints

Say a quant researcher on the desk hands you a vague idea, something like 'see if this signal helps.' How would you turn that into a concrete experiment plan?

ability to translate ambiguous requests from non-ML domain experts into a structured research plan

If you trained a model and its offline metrics looked great but you were told it needs to run in a live trading system with strict latency limits, what tradeoffs would you consider before deploying it?

judgment about the gap between research metrics and production viability in a latency-sensitive environment

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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="8" 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="8" 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="8" 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="8" 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="8" 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="8" 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="8" 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="9" 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="9" 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="9" 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="9" 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="9" 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="9" 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="9" 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="9" 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="10" 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="10" 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="10" 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="10" 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 (annualized) is $300,000 per year.

Apply at Jump Trading

Campus AI Research Engineer - Deep Learning (Intern)

Likely interview questions for this role

Practice this interview out loud.

The full job description

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