Quant jobs / Graham Capital Management

Machine Learning Engineer

An ML engineering role in the data science team of a global macro and trend-following fund. The team builds the data and model services that quant researchers and portfolio managers rely on.

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.

Explore large market, news and alternative datasets for usable signals
Build time-series and forecasting models that support quant strategies
Deploy models and services in containerised environments
Turn research findings into clear points for traders and researchers

What they ask for

Required

Degree in computer science, engineering, OR or another quantitative field
3+ years of ML and statistics on large unstructured data
Python (preferred) or C++
Spark/Scala or SQL
Containerised deployment
Time series and panel data methods
Clear communication

Preferred

Production code for multi-client model-serving systems

Education: Undergraduate degree or higher in computer science, engineering, operations research or another quantitative field
Experience: 3+ years of hands-on ML and statistics on large datasets
Languages and tools: Python
C++
Spark
Scala
SQL

How to prepare

Build a forecasting pipeline on a large time-series dataset with proper validation
Practise Spark or similar distributed processing on a big dataset
Package a model as a container and serve it
Review panel-data and time-series methods

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 Graham Capital Management’s site before applying: requirements, location and pay can change after we check them.

View the posting on Graham Capital Management’s site ↗

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