Hiring nowLearning theory and statistical machine learning

Machine Learning Research Scientist

Also called ML Scientist · Research Scientist, Machine Learning

A Machine Learning Research Scientist develops and tests learning methods against explicit hypotheses. They give the company evidence about what works and why, so modelling bets rest on results that others can reproduce.

What they do

The work starts with a question and a hypothesis. They read the literature, then design experiments that could show the idea is wrong. They write training and evaluation code in Python, using PyTorch for deep models and scikit-learn for baselines. They run ablations to find which part of a method earns its place. They check results across seeds and held-out tasks, analyse why a method generalises or fails, and write it up so a colleague can rerun everything. They also tell the team when an idea does not hold up.

What a strong hire delivers

In the first months, a strong hire delivers a clear research question tied to a real company need, and a baseline the team trusts. They produce a reproducible package of paper, code and ablations that another person can rerun. They show where a method generalises and where it breaks. They also leave the team better at experimental design and at judging claims.

How you can tell they are good

Ask them to walk through one piece of research from hypothesis to result. Strong candidates explain what would have falsified it, which ablations mattered and what failed. Ask how they know a gain is not noise or leakage. Be wary of vague claims, results on one dataset only, or code nobody else has run.

Why it is hard to fill

Titles are loose. The same work sits under scientist, researcher or applied scientist, and many people with the title mainly build products or tune models. Real research skill is rarer than the title suggests. Backgrounds are mixed too: statistics, mathematics and computer science all produce good people. Frontier labs and universities compete for the strongest candidates, so a clear research agenda matters in the pitch.

Where to find them

Look in academic research groups, especially statistics, mathematics and computer science departments, and among postdoctoral researchers moving toward industry. Industry research teams and applied science groups are another source. Model providers, AI start-ups and research-led product companies employ many. Check publication records, released code and replication work, not only job titles.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for a Machine Learning Research Scientist

  1. Tell us about a research idea you tested that did not work. What did you learn?

    A strong answer states the hypothesis, the experiment and the evidence against it, then what changed afterwards. Worry if they only describe successes, blame the data without checking, or cannot say what would have counted as failure.

  2. How do you decide whether a reported improvement is real rather than noise or leakage?

    Look for multiple seeds, confidence intervals, clean train and test separation, tuned baselines and held-out tasks. Worry if they rely on a single run, compare against weak baselines, or tune on the test set.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We are hiring a Machine Learning Research Scientist to develop and test learning methods against explicit hypotheses. You will work with engineers and product colleagues to turn open questions into evidence, and to share results the whole team can build on.

What you will do

  • Form clear hypotheses and design experiments to test them
  • Develop and evaluate learning methods in Python and PyTorch
  • Run ablations and analyse generalisation on held-out tasks
  • Produce reproducible papers, code and ablation packages
  • Review and replicate colleagues' results
  • Advise the team on which ideas are worth pursuing

What you will bring

  • Demonstrated research work, such as papers, preprints or released code
  • Strong grounding in probability, statistics and linear algebra
  • Experience designing controlled experiments and ablations
  • Hands-on deep learning experience in PyTorch
  • Evidence that others have reproduced or built on your results
  • Clear written communication of methods and limits

Nice to have

  • Experience with optimisation or learning theory
  • Published replication or negative-result work
  • Experience moving research into production systems

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • Probability and statisticsAdvancedQuantify variability and statistical evidence
  • Linear algebra and optimisationAdvancedReason about matrix operations and objective functions
  • Experimental designWorkingChoose comparisons that identify credible effects
  • Deep learningWorkingBuild and diagnose trainable neural models

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

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