Quant jobs / Millennium

Quantitative Developer – Compute & ML Platform

A platform engineering role in a central liquidity strategies group, building a distributed dataframe layer and ML infrastructure for researchers. It supports both research and live trading models.

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.

Add operators and optimisations to the in-house lazy distributed dataframe API
Build ML pipelines for training, tuning, serving and retraining models
Improve reliability, monitoring and multi-user isolation of compute
Design abstractions that survive changes of compute backend

What they ask for

Required

6+ years Python and distributed systems in quant finance
Distributed frameworks (Ray, Dask, Spark, Slurm)
PyTorch and XGBoost workflows
Git, Linux, Bash, CI/CD
Platform abstractions used by others
Collaboration with researchers

Preferred

kdb+/PyKX
Polars, Arrow, columnar engines
Cash equities
Kubernetes and cloud

Education: Bachelor's in computer science, maths, financial engineering, operations research or similar
Experience: 6+ years in Python and distributed systems in quantitative finance
Languages and tools: Python
kdb+/q
Kubernetes
Markets: equities

How to prepare

Build a lazy dataframe wrapper over Arrow to see how query planning works
Run distributed training and tuning with Ray
Set up model retraining with validation gates
Learn kdb+/PyKX basics

What to study

Apply

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

View the posting on Millennium’s site ↗

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