Machine Learning Research Scientist CV Example & Template
A complete two-page CV for a machine learning research scientist with a PhD, moving from academia to an industry lab, followed by what research leads and hiring committees check first. Use the structure, then replace the papers, benchmarks, compute and code with your own, since reviewers will read the work itself.
Last reviewed October 2026
Example Machine Learning Research Scientist CV
Dr Elena Kowalski
Machine Learning Research Scientist, language models and efficient training
London, UK
Profile
Machine learning researcher with a PhD and two years of postdoctoral work on efficient fine-tuning and evaluation of language models. I have five papers at top conferences, three as first author, and I write research code that other people can run. I am looking for a research scientist role in an industry lab where results go into products, with a focus on training efficiency and reliable evaluation.
Experience
Postdoctoral Researcher, Machine Learning
Oct 2023 – Present
Department of Computer Science, University of Edinburgh · Edinburgh
- Developed a parameter-efficient fine-tuning method that matched full fine-tuning on four benchmarks while updating under 1% of the weights.
- Cut training cost for 7B-parameter experiments by about 40% through mixed precision, gradient checkpointing and a better data loader, on a cluster of 32 GPUs.
- Designed an evaluation suite of 1,200 held-out items to detect benchmark contamination, now used by two other groups in the department.
- Wrote the method and ablation sections of two papers and led the rebuttal for one, which was accepted at a leading conference.
- Supervise three MSc projects and one PhD student, and run a weekly paper reading group of about 25 people.
Research Intern, Language Modelling
Jun 2022 – Dec 2022
Aurelia AI Research · London
- Built a retrieval-augmented baseline that improved question answering accuracy by 6 points over the team's previous model.
- Ran 300 controlled experiments with tracked seeds and configs, so every result in the internal report could be reproduced.
- Presented findings at the lab's research meeting and handed over a documented code base to two engineers.
PhD Researcher, Machine Learning
Oct 2019 – Sep 2023
University of Edinburgh · Edinburgh
- Studied generalisation of neural sequence models under distribution shift and published three first-author papers.
- Open-sourced a library for evaluation of robustness that now has about 900 stars and is cited in four papers by other groups.
- Taught tutorials for a 150-student machine learning course for three years, and marked coursework with a published rubric.
- Won a best poster award at a workshop on efficient deep learning.
Education
PhD Informatics (Machine Learning)
2019 – 2023
University of Edinburgh
Thesis on robustness and generalisation of sequence models
MEng Mathematics and Computer Science, First
2015 – 2019
Imperial College London
Skills
PyTorch and JAX · Language models · Fine-tuning and evaluation · Distributed training · Experimental design · Statistical testing · Python and CUDA basics · Research writing · Reproducible code · Literature review · Mentoring · Weights and Biases
Selected publications
- Five papers at NeurIPS, ICML and ICLR (three first author); full list available on request
- Reviewer for three conferences, 2022 to 2025
Open source
- Maintainer of a robustness evaluation library for sequence models, 900 stars
Why this Machine Learning Research Scientist CV works: what shortlisters check first
Publications, with your role and venue
Research leads begin with the paper list. Give the venue, the year and your position among the authors, and mark which were first author. Pick two or three papers closest to the job and explain each in a line. A shorter list of strong work is better than a long list of workshop posters.
Experiments you can describe in detail
Interviews go deep on method. Show the question, the baseline, the ablations and the size of the effect, with numbers. State compute scale honestly: number of GPUs, model size and training time. Claims of improvement without baselines or variance will be questioned by any reviewer.
Engineering that makes research usable
Industry labs hire researchers who also write code others can run. Mention open-source libraries, tracked experiments, tests and handovers to engineers. Say which frameworks you use, such as PyTorch or JAX, and how you manage configs and seeds so results are reproducible.
Research taste and direction
Show the line of work you are building, not only a list of tasks. A sentence on why your problem matters and where it goes next helps committees judge fit. Link your agenda to the lab or team you apply to, using its public papers as evidence you have read them.
Collaboration, mentoring and review
Labs are teams. Include supervision, reading groups, reviewing for conferences and work with engineers or product teams. Give counts and dates. If you move from academia, show one project where the result was used by someone else, even inside your own department.
Key skills recruiters look for
How to write each section of a Machine Learning Research Scientist CV
Profile
State your area, degree, number of papers and one engineering strength. Then say what role you want and what you will work on. Skip general phrases about passion for AI and name the problem you work on instead.
Experience
For each role give the research question, your contribution, and the result with numbers. Show scale: parameters, GPUs, data size or number of runs. Cut teaching and administration to a line each, unless the job is partly academic.
Publications
List papers in reverse order with the authors, title, venue and year, or link a page where the full list lives. Mark first author and oral or spotlight status only if true. Give preprints their own label so readers know they are not peer reviewed.
Skills
Name frameworks, methods and tools you have used in real projects. Group them as models, training, evaluation and engineering. Leave out courses and libraries that you have only imported once, since an interviewer may ask about any item on the list.
Format
Two pages for industry, longer for academic fellowships. Use a clean layout with no photographs or graphics. Link to code and papers through your application form or a plain URL on your profile page rather than long link lists in the CV.
More example bullet points
Strong Machine Learning Research Scientist CVs lead with measurable impact. Here are examples of the kind of bullets that make recruiters stop scrolling:
Published 4 peer-reviewed papers at NeurIPS and ICML, with primary paper cited 200+ times within 18 months of release
Developed novel fine-tuning approach reducing instruction-following error rate by 34% on a held-out benchmark with 40% less training compute
Built research-to-production pipeline that cut model iteration cycles from 3 weeks to 4 days, enabling 3× more experiments per quarter
Common Machine Learning Research Scientist CV mistakes
- Listing every workshop poster and preprint as if they carried the same weight as top conference papers.
- Claiming an improvement without naming the baseline, the benchmark or the variance.
- Leaving out compute scale, so the reader cannot tell whether you trained a small model or a large one.
- Describing a team result without saying which part you designed, ran or wrote.
- Sending a five page academic CV to an industry lab that asked for two pages.
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How to tailor your Machine Learning Research Scientist CV for each application
Every Machine Learning Research Scientist job posting is different. The skills they prioritize, the seniority they expect, the setting — all of it shapes what your CV should emphasize. A CV that gets an interview at one employer can miss at another, even for the same job title.
The fastest way to tailor your CV is to paste the job description into NextCV. The AI reads the posting, maps it to your experience, and rewrites your bullets to match what the employer is scanning for. 1 free preview, no signup required — you only pay when you want a clean PDF download. Sign up free for 3 more.
Machine Learning Research Scientist CV FAQ
What should a machine learning research scientist CV include?
A short profile, research and industry experience with methods and results, publications with venue and authorship, education, skills and open-source work. Add reviewing and supervision. Two pages is usual for industry.
Do I need a PhD to be a research scientist?
Most research scientist posts ask for a PhD or equivalent research record. Some accept a masters degree with strong papers or open-source work. If you lack a PhD, show first author publications, reproducible code and results that others have built on.
How should I list publications on my CV?
Use reverse chronological order with authors, title, venue and year, and mark your own position. Keep preprints separate from peer reviewed papers. For a two page CV, choose the most relevant papers and link the full list from your application.
How do I move from academia to an industry lab?
Shorten the CV, lead with results and engineering, and show work with scale. Mention collaborations and code others use. Explain how your research agenda would fit the lab, and cut teaching and committee detail to a few lines.
Should I include blog posts or side projects?
Include them when they show real technical depth, such as a reproduction of a paper, an open-source library with users or a clear write-up of an experiment. Leave out tutorials copied from courses. One strong project is better than five minor ones.