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Machine Learning · Internship

Machine Learning Intern at Evore Labs.

Help turn learning research into systems that train, evaluate and serve models reliably.

Stipend
£1,300 / month
Duration
2 months
Work
Remote
Location
Remote-first · UK
The Role

What you will own.

You will work at the seam between research and production — taking model ideas from a scrappy notebook to something that trains reproducibly, is evaluated honestly, and can be trusted inside a governed pipeline. This is an engineering-heavy ML role: you care as much about data leakage, evaluation rigour and reproducibility as about model architecture. You will own a real pipeline component end to end and see it used.

Responsibilities

  • Build and maintain training, evaluation and inference pipelines.
  • Design honest evaluation protocols with strict train / validation / test hygiene.
  • Track experiments so every result is reproducible from a single command.
  • Profile and optimise data loading, training throughput and memory.
  • Work with researchers to productionise promising models.

Qualifications

  • Solid software engineering fundamentals and fluent Python.
  • Working knowledge of the modern ML stack (PyTorch or JAX).
  • Understanding of overfitting, data leakage and evaluation pitfalls.
  • Care for correctness, latency and cost — not just accuracy.
  • Comfortable collaborating through Git and code review.

Nice to have

  • Experience with financial, time-series or streaming data.
  • Familiarity with experiment tracking or feature stores.
  • Distributed or mixed-precision training experience.
Learning

What this track teaches.

You will learn

  • What it takes to make ML reproducible and trustworthy.
  • Evaluation design that does not fool you.
  • Moving models from notebook to governed production.
  • Performance engineering for training and inference.
Tools

The stack around the work.

Python
PyTorch
JAX
Weights & Biases
Ray
Docker

Apply

Send the work that proves the fit.

Tell us what you have built, tested or discovered. A researcher or engineer reads every application.

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