Quant jobs / DRW

Machine Learning Specialist

An ML engineering role building the systems that serve, train and monitor machine learning models at a trading firm. The focus is production reliability and performance.

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.

Build systems to serve ML models at scale
Speed up training and inference of deep learning models
Maintain feature pipelines, feature stores and model stores
Set up monitoring and retraining for live models
Build tooling for reproducible research

What they ask for

Required

5+ years building ML software
Machine learning and deep learning concepts
Deploying and monitoring production models
MLOps tooling
TensorFlow, PyTorch, TensorRT or ONNX
GPU programming (CUDA or OpenCL)
Inference optimisation

Preferred

Distributed systems
Multi-threaded programming
Hardware/software interaction

Education: Bachelor's or Master's in computer science, software engineering or equivalent
Experience: 5+ years building software for ML systems
Languages and tools: Python
CUDA
PyTorch
TensorFlow
ONNX
TensorRT

How to prepare

Deploy a model behind an API with monitoring and drift checks
Profile and quantise a model for faster inference
Build a feature pipeline with versioned datasets
Learn basic CUDA

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

View the posting on DRW’s site ↗

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