Software Engineer, Research – Cumberland Systematic
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
The team works across Chicago and London. How do you keep collaboration and handoffs smooth across time zones?
What they are checking: Whether they write clearly and document so work does not stall while the other office sleeps.
You are expected to work across the full stack. Tell me about a time you worked well outside your strongest area.
What they are checking: Range and willingness to pick up whatever the team needs.
Tell me about a time you made a data-heavy job much faster.
What they are checking: Practical performance skills in the kind of large-data work research depends on.
What would you do
A strategy behaves differently in production than it did in research. How do you find out why?
What they are checking: A structured approach comparing data, latency, fills and code versions to locate the gap.
About the work
Cumberland Systematic trades mid and high-frequency delta one strategies 24/7. What does high availability mean for a system like that, and how would you design for it?
What they are checking: Whether they understand failover, redundancy and state recovery when there is no market close to fix things.
Walk me through how you would build a research pipeline that lets quants backtest strategies on large amounts of tick data.
What they are checking: Design for data storage, reproducibility and speed that matches how researchers actually work.
How do you make sure a backtest and live trading use the same logic so results are trustworthy?
What they are checking: Awareness of look-ahead bias, timing differences and shared code paths that make research honest.
What is delta one, and why might a systematic team trade it on crypto venues?
What they are checking: Basic understanding of the products the team trades, so they can talk with researchers in their terms.
How would you handle a crypto exchange connection that drops and reconnects with gaps in market data?
What they are checking: Thinking about sequence gaps, snapshots and keeping strategies safe while data is incomplete.
How do you decide which language or tool to use for a new research component?
What they are checking: Whether they pick tools for the job and the team, weighing speed of development against runtime speed.
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Practice this out loudThe full job description
As published by DRW.