Quant jobs / Tower Research Capital

Machine Learning Engineer – Research Platform

An engineering role building the machine learning research platform used by quant researchers at a systematic trading firm. It covers training, experiment tracking and large-scale backtesting infrastructure.

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 scalable training and simulation infrastructure across HPC and cloud
Improve experiment tracking, model versioning and data lineage
Create tooling for feature engineering and dataset generation
Improve GPU scheduling and compute efficiency
Add monitoring and debugging for ML workloads

What they ask for

Required

2+ years building distributed systems
Strong Python
Linux HPC or cloud experience
GPU workloads and PyTorch/TensorFlow/JAX
Data pipeline optimisation
Debugging across system layers

Preferred

Internal ML platform experience
Experiment tracking and workflow orchestration
Docker and Kubernetes
Exposure to quantitative finance

Education: Not stated.
Experience: 2+ years in large-scale distributed systems
Languages and tools: Python

How to prepare

Run a distributed training job on GPUs and profile it
Build a small experiment-tracking and reproducibility setup
Learn Docker and Kubernetes basics
Understand how backtesting pipelines are structured

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.