Quant jobs / Voleon

Member of Research Staff – Optimization

A research role on the optimisation systems that turn return forecasts into trades, balancing risk, costs and constraints. It sits in the research group at an ML-driven hedge fund.

What you’d work on

Our reading of the posting: what a new hire in this role would most likely spend their first months on.

Improve constrained optimisation that sets trade decisions
Refine the problem formulation to get better trades
Speed up solvers in production code
Test changes in simulation and live trading

What they ask for

Required

Numerical methods and optimisation solvers
Convex or nonconvex optimisation
Stochastic control and reinforcement learning
Strong mathematics
Production Python or C++
PhD-level coursework

Preferred

PhD in a relevant field

Education: PhD-level coursework required; PhD in a relevant field preferred
Experience: Applied optimisation background; finance experience not required
Languages and tools: Python
C++

How to prepare

Build a mean-variance optimiser with transaction costs using a convex solver
Study stochastic control for trade scheduling
Practise implementing solvers efficiently in C++ or NumPy
Learn how forecasts, risk and costs enter a portfolio objective

What to study

The Portfolio and Factor Quant roadmap sets out the stages for this kind of role, with free resources for each.

Open the roadmap

Apply

Read the full posting on Voleon’s site before applying: requirements, location and pay can change after we check them.

View the posting on Voleon’s site ↗

SelfMadeQuant wrote this summary from the employer’s public posting, last checked 6 November 2026. SelfMadeQuant isn’t affiliated with Voleon and doesn’t handle applications.