Quant jobs / Optiver

Machine Learning Performance Engineer

An engineering role making deep learning training and inference fast for futures trading models. It sits in a market maker's AI initiative working closely with 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 training and inference pipelines for deep learning
Profile and remove GPU and framework bottlenecks
Extend open-source deep learning frameworks where needed
Learn the trading systems the models plug into

What they ask for

Required

Low-level CUDA programming
Deep learning framework internals
Computer architecture
C++ and Python

Preferred

JAX, XLA and Flax
Triton, CUB, cuDNN, cuBLAS
Linux systems programming
Large-scale distributed training
Open-source ML contributions

Education: No degree level stated.
Experience: Experienced; years not stated
Languages and tools: C++
Python
CUDA
JAX
PyTorch
Markets: futures

How to prepare

Write and profile a custom CUDA kernel
Read PyTorch or JAX internals and trace one operator end to end
Benchmark training throughput and identify bottlenecks
Study how deep models are used 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 Optiver’s site before applying: requirements, location and pay can change after we check them.

View the posting on Optiver’s site ↗

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