Quant jobs / Tower Research Capital

Machine Learning Performance Engineer – Inference

A core engineering role making ML inference run at microsecond-level latency for trading models. It sits between quant research and production systems.

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.

Benchmark inference on CPUs, GPUs and FPGAs
Write and tune custom GPU kernels
Compress models through quantisation, pruning and distillation
Remove memory and interconnect bottlenecks
Work with ML researchers, FPGA and datacentre engineers on deployments

What they ask for

Required

2+ years optimising deep-learning inference
PyTorch or JAX internals
Python and C++
Custom GPU kernel development
GPU microarchitecture
Benchmarking across hardware
Profiling tools

Preferred

Inference on FPGAs and ASICs

Education: Not stated.
Experience: 2+ years in latency-sensitive inference; finance experience not required
Languages and tools: Python
C++

How to prepare

Write and profile a CUDA or Triton kernel
Quantise a small model and measure accuracy versus latency
Learn GPU memory hierarchy and warp scheduling
Study TensorRT or ONNX deployment

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

View the posting on Tower Research Capital’s site ↗

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