Quant jobs / Voleon

Senior Machine Learning Engineer

An engineering role embedded in research teams, turning prototype models into production code for trading strategies. It sits between PhD researchers and the engineering organisation.

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.

Turn research prototypes into tested production code
Build ingestion, feature and validation pipelines
Chase subtle data-quality problems in research and production
Create tools that speed up model experimentation
Guide junior engineers on standards

What they ask for

Required

5+ years of professional software engineering
Strong CS fundamentals
Maths for stats, linear algebra and optimisation
Deep Python
NumPy, Pandas, SciPy, scikit-learn or PyTorch
Distributed ML systems
Linux

Preferred

Experiment management and ML workflow orchestration
Model serving, feature stores, distributed training
Performance profiling
Financial time-series exposure

Education: Bachelor's or higher in computer science, applied maths, statistics or similar
Experience: 5+ years of professional software engineering
Languages and tools: Python
R
C/C++

How to prepare

Build an ML pipeline with reproducible experiments and data validation
Practise profiling and speeding up NumPy or PyTorch code
Revise linear algebra, optimisation and probability
Learn feature engineering for financial time series

What to study

The NLP and ML Quant Researcher 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.