Hardware Machine Learning PhD Research Internship
Posted Sep 30, 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 PhD research and how it might apply to running ML inference on custom hardware.
What they are checking: Whether they can connect academic work to real low-latency inference problems.
How would you present a negative result to the team in a way that's still useful?
What they are checking: Honest scientific communication, since owning a project includes reporting what didn't work.
What would you do
You read a paper claiming a big speedup. How do you decide if it will help our systems?
What they are checking: Whether they judge research by real-world constraints and engineering cost, as the posting asks.
What do you know about RTL design, and how would you work with hardware engineers who know much more?
What they are checking: Humility and willingness to learn hardware fundamentals from experienced RTL developers.
How would you scope a research project so you finish with a working prototype or benchmark in one summer?
What they are checking: Whether they can plan research to deliver something useful in a short time.
About the work
How would you quantize a model for an FPGA without losing too much accuracy?
What they are checking: Practical knowledge of quantization methods, which the posting names directly.
What's the difference between optimizing a model for throughput on a GPU and for latency on custom hardware?
What they are checking: Whether they understand that low-latency inference changes which models and tricks make sense.
How would you use neural architecture search when every model must meet a strict latency budget on hardware?
What they are checking: Whether they can include hardware constraints in model search, not just accuracy.
What makes a good benchmark for ML inference latency, and what makes a misleading one?
What they are checking: Rigor in measurement so the team can trust and build on their results.
How do you implement and verify that a model running in hardware matches the software reference?
What they are checking: Understanding of bit-exact verification and numerical differences between platforms.
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Practice this out loudThe full job description
As published by IMC Trading.