Quant jobs / IMC Trading

Hardware Machine Learning Engineer

An engineer putting machine learning models onto custom hardware such as FPGAs for low-latency trading. It sits in a team that designs the hardware, software and infrastructure together.

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.

Co-design ML models with quants treating latency and precision limits as design inputs
Translate model needs into hardware architecture decisions
Implement, verify and deploy inference designs from prototype to production
Assess new work on quantisation and architecture search for real gains

What they ask for

Required

Hardware design trade-offs (pipelining, fixed-point)
FPGA or ASIC mapping of ML models
VHDL, SystemVerilog, HLS or similar
Neural network and inference fundamentals
PyTorch or TensorFlow
Python or C++

Preferred

ML compiler tools (MLIR, TVM, XLA)
Latency-sensitive systems background
Verification methods (UVM, Cocotb)
MS or PhD in EE, CS or physics

Education: Advanced degree is a plus; equivalent industry or research depth accepted.
Experience: Researchers and experienced engineers from any background; trading experience a bonus
Languages and tools: Python
C++
VHDL
SystemVerilog
PyTorch
TensorFlow

How to prepare

Implement a small neural network on an FPGA using HLS or hls4ml
Study fixed-point arithmetic and quantisation effects on accuracy
Learn latency budgeting for inference pipelines
Write a simple testbench in SystemVerilog or Cocotb

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.