Deep Learning Research Intern - Summer 2027 - Chicago, New York
Posted Oct 1, 2026 · We last checked this listing on Oct 6, 2026
Likely interview questions for this role
Written from this job description, not a generic list. Each one notes what the interviewer is really checking.
About your past
Tell me about your research. What question were you trying to answer and what did you find?
What they are checking: Whether they think like a scientist and can explain their work clearly.
Tell me about a time an experiment failed. What did you do next?
What they are checking: Persistence and good judgment about when to change direction in research.
What recent deep learning paper excited you, and what would you test to see if it holds up?
What they are checking: Real passion for advancing the field and a critical eye toward published claims.
What would you do
Your model's validation loss improves but the real-world metric doesn't. What do you check?
What they are checking: Whether they question the setup, metric and data splits instead of just tuning more.
Your mentor suggests a direction you think won't work. How do you handle it?
What they are checking: Whether they can disagree respectfully with evidence and stay open to being wrong.
About the work
How does a transformer handle long sequences, and what limits it?
What they are checking: Real understanding of transformers, one of the research areas the posting names.
What makes financial time series hard for deep learning compared with images or text?
What they are checking: Whether they can reason about noise, non-stationarity and weak signal without finance background.
How would you use representation learning on a large unlabeled dataset?
What they are checking: Familiarity with self-supervised and representation learning methods the posting mentions.
How do you design an experiment so you can trust what you learn from it?
What they are checking: Scientific rigor in ablations, baselines and controlled comparisons.
When would reinforcement learning make sense instead of supervised learning?
What they are checking: Whether they understand when each method fits, rather than using the newest idea by default.
How do you train efficiently when you have a lot of data and limited GPU time?
What they are checking: Practical skill using training infrastructure well, not just designing models.
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Practice this out loudThe full job description
As published by IMC Trading.