Quant jobs / Tower Research Capital

Machine Learning Research Engineer

An applied engineering role that acts as the first user of the firm's internal ML research platform. It tests the stack end to end and feeds back to infrastructure builders and 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.

Run complex models through the full training and inference pipeline
Build abstractions that make prototyping easier
Use AI agents to automate experiments and stress-test clusters
Document performance findings and recommend platform changes

What they ask for

Required

Strong Python and software design
PyTorch or TensorFlow
Scaling ML across GPUs (Ray, Dask, PyTorch Distributed)
LLM and agent tooling
Profiling bottlenecks

Preferred

Quantitative finance exposure
Large time-series or simulation data
Benchmarking experience

Education: Not stated.
Experience: Experience in applied ML and distributed compute; years not stated
Languages and tools: Python

How to prepare

Scale a training job with Ray or PyTorch Distributed
Build a clean Python API around an ML workflow
Try an agent-driven experiment loop
Profile GPU and data-loading bottlenecks

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 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.