Quant Development
Quant Developer and Research Engineering Recruitment
Search for the engineers who make research possible: backtesting and simulation frameworks, data pipelines, portfolio tooling and the path from a research idea to a production trading system.
Quant development sits between two teams that measure quality differently. Researchers judge the platform on how fast an idea can be tested; production judges it on whether it holds up under live risk.
The strongest candidates are comfortable being accountable to both, and we qualify for that explicitly rather than treating the role as generic backend engineering.
01
Research infrastructure
Most of the value in this function is invisible from outside: the framework that lets a researcher run a credible backtest in minutes instead of a day.
- 01Backtesting and simulation frameworks designed, rebuilt or materially extended
- 02Handling of survivorship bias, point-in-time data and transaction cost modelling
- 03Data pipelines for market, reference and alternative data at scale
- 04Research tooling and notebooks used daily by researchers, not shipped and abandoned
- 05Portfolio and risk tooling supporting live decision-making
02
Engineering depth
Python is the common language of research, but the systems underneath are rarely pure Python. We look at where a candidate has needed C++ or Rust, how they have reasoned about distributed systems and what they have profiled and improved.
We also look at ownership: who was called when the pipeline failed at four in the morning, and what changed afterwards.
03
Research-to-production workflows
The handover between research and production is where teams lose weeks. We ask candidates to describe the workflow they have worked in, what they would change about it and what they have already changed.
That answer tells us more about seniority than a job title does.
04
Roles we cover
- 01Quant Developers working directly with research teams
- 02Research Engineers owning frameworks, tooling and data platforms
- 03Data Engineers handling large-scale market and alternative data
- 04Machine Learning Engineers productionising research models
- 05Platform leads building out a research technology function
Common technical ground
- Python
- Modern C++
- Rust
- Distributed Systems
- kdb+/q
- Linux
- Backtesting
- Data Pipelines