Quant jobs / IMC Trading

Machine Learning Engineer

An engineer building the systems that train and serve large ML models for trading research. Work splits between research tooling and GPU performance, and sits close to quant researchers.

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 distributed training pipelines for large datasets and models
Automate experiments, tuning and retraining for researchers
Optimise training and inference on GPUs
Build low-latency inference pipelines for live predictions
Improve ML framework internals for the firm's workloads

What they ask for

Required

3+ years in ML training or inference systems
Production Python engineering
ML fundamentals beyond high-level APIs
Performance measurement mindset
Depth in distributed training, GPU optimisation, inference or numerics
Collaboration with researchers

Preferred

Cloud and orchestration tools
Open-source contributions

Education: Not stated.
Experience: 3+ years in machine learning training or inference systems
Languages and tools: Python
PyTorch
TensorFlow
JAX
CUDA
Triton

How to prepare

Train a model across multiple GPUs and profile the bottlenecks
Write a custom GPU kernel in Triton or CUDA and benchmark it
Study checkpointing, parallelism strategies and data loading
Learn the basics of low-precision numerics and batching

What to study

The HFT Quant Developer roadmap sets out the stages for this kind of role, with free resources for each.

Open the roadmap

Apply

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

View the posting on IMC Trading’s site ↗

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