Quant jobs / IMC Trading

Hardware Machine Learning PhD Research Internship

A PhD internship on low-latency ML inference and hardware acceleration, with a self-contained project. It sits within the engineering group that builds custom hardware for trading.

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.

Own a research project on accelerating ML inference
Work with hardware engineers to implement and verify designs
Assess recent work on quantisation and architecture search against real constraints
Present results and leave a prototype or benchmark

What they ask for

Required

Enrolled in a PhD in EE, CS, physics or similar
Hardware trade-offs for FPGAs or ASICs
VHDL, SystemVerilog, HLS or ML-to-hardware tools
Neural network and inference basics
PyTorch or TensorFlow
Python

Education: Currently enrolled in a PhD in electrical engineering, CS, physics or a related field.
Experience: Current PhD student
Languages and tools: Python
VHDL
SystemVerilog
PyTorch
TensorFlow

How to prepare

Run a small neural network through an HLS flow such as hls4ml or FINN
Study quantisation and its effect on model accuracy
Learn RTL basics and simple pipelining patterns
Prepare to present a project clearly to mixed audiences

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.