Quant developer interviews look like software engineering interviews at first glance — and then veer. There is usually more math, more performance obsession, and a systems focus that begins at the network interface rather than at the API gateway.
Coding: less breadth, more precision
Expect the classic format — 45 minutes, shared editor, talk through your approach — but with a twist: correctness and clarity matter more than the cleverness of your solution.
- Arrays, strings, hash maps, two pointers, sliding windows, and basic graph traversal cover most questions.
- Probability-adjacent problems appear: simulate a weighted random choice, implement reservoir sampling, estimate Pi with Monte Carlo.
- You may be asked to optimize. Know your data structures' memory layout: cache lines, pointer chasing, vectorization-friendly loops.
Dynamic programming shows up less than at big-tech companies, and when it does it is usually simple. Do not spend your prep month grinding DP graphs; spend it writing clean code quickly while explaining trade-offs.
Systems: where finance diverges from tech
- Latency as a feature. Questions about lock-free queues, ring buffers, busy-waiting versus blocking, and why a malloc in a hot path is a problem. Understand TCP tuning basics: Nagle, TCP_NODELAY, kernel bypass concepts.
- Time. Clock sources, timestamp precision, and why wall-clock time is not good enough for measuring latencies.
- Reliability. Failover, hot standby, and how a trading system degrades when the market data feed drops for 400 milliseconds.
- Order and idempotency. Exactly-once semantics in a distributed setting, sequence numbers, and reconciliation.
The math undercurrent
Even engineering roles often include one math or probability segment. Common shapes:
- Expectation puzzles with a twist (tests whether you think in linearity and symmetry rather than brute-force enumeration).
- Basic statistics: are these two strategies' returns meaningfully different, given this sample size?
- Discrete probability with coding: simulate and estimate, then reason about variance and confidence.
You do not need stochastic calculus for most quant dev roles. Brush up on it if the team touches pricing libraries.
Domain questions by specialty
- Market data: feed handlers, protocol parsing (FIX, ITCH variants), snapshot versus incremental updates, gap detection.
- Execution: order lifecycle, venue behavior basics, and how to reason about partial fills.
- Research engineering: backtesting pitfalls — lookahead bias, survivorship bias, and why simulating a fill at the last price is fiction.
How to prepare in six weeks
- Weeks 1 to 2: 60 to 80 standard coding problems, all timed and out loud.
- Weeks 3 to 4: systems — concurrency, memory, networking, plus one serious performance project where you profile and improve your own code.
- Weeks 5 to 6: domain reading and mocks. Build or explain a miniature market data pipeline end to end.
The candidate who wins is rarely the one who solves the hardest problem. It is the one who writes clean code, talks about their trade-offs, and asks the right clarifying questions before typing.
What languages to use
C++ for latency-oriented teams, Python for research infrastructure, and increasingly Rust in greenfield systems. Java and Go persist in platform and banking infrastructure. Match the firm's stack — check the job description, and if in doubt, ask the recruiter.