Research Scientist résumé: what employers are asking for right now
We read 118 open research scientist postings on employers' own job boards, at companies including Anthropic, OpenAI and Scale AI. Of the 63 whose requirements we have read, the skills asked for most are Python (92%), PyTorch (73%) and Machine Learning (68%) — so those belong near the top of a research scientist résumé, in the words the posting uses. The median posting asks for 3+ years of experience.
From 118 open postings on employers’ own job boards, as of .
Skills research scientist postings ask for
| Skill | Share of postings | Bar |
|---|---|---|
| Python | 92% | |
| PyTorch | 73% | |
| Machine Learning | 68% | |
| Deep Learning | 57% | |
| Reinforcement Learning | 38% | |
| LLMS | 30% | |
| Model Evaluation | 24% | |
| Rlhf | 17% | |
| Distributed Training | 16% | |
| Large Language Models | 16% | |
| Transformers | 14% | |
| Data Pipelines | 13% | |
| Natural Language Processing | 13% | |
| Software Engineering | 13% | |
| SQL | 13% |
Share of the 63 postings whose requirements we have read.
A skills section built from those postings
List only what you can talk about in an interview, and use the posting’s own words for it — a screener searching for “Python” will not match a synonym. Ordered by how often employers ask:
Skills
Python · PyTorch · Machine Learning · Deep Learning · Reinforcement Learning
LLMS · Model Evaluation · Rlhf · Distributed Training · Large Language Models
Transformers · Data Pipelines · Natural Language Processing · Software Engineering · SQL
- Line 1: asked for by a third or more of postings.
- Line 2: often asked for.
- Line 3: sometimes asked for.
Sample research scientist résumé
An illustrative example, not a real person: the employers and results are made up. The skills are the ones the postings above ask for, so it shows how those requirements read on a page.
Senior Research Scientist
Research scientist focused on building and evaluating machine learning systems, with experience in Python, PyTorch, and large-scale model training. Worked on reinforcement learning, LLMs, and data pipelines to improve model quality, reliability, and deployment readiness.
Experience
Senior Research Scientist · AI platform company supporting enterprise automation teams
2022 – present
- Built PyTorch training workflows for large language models, cutting experiment turnaround time by 35% through cleaner data pipelines and reproducible runs.
- Developed model evaluation suites for ML and NLP tasks, improving offline metric tracking and exposing regressions before release.
- Applied reinforcement learning and RLHF methods to align generation behavior, increasing task success rates on internal benchmarks.
- Partnered with software engineering teams to harden distributed training jobs across GPU clusters and reduce failed runs in production.
Research Scientist · Applied AI startup serving regulated industries
2020 – 2022
- Trained deep learning models in Python for text classification and retrieval, raising F1 by 8 points on a customer support dataset.
- Implemented transformer-based baselines and comparison studies, helping teams select architectures for low-latency inference.
- Built SQL-backed analysis workflows to monitor data quality, label drift, and evaluation slices across multiple model versions.
- Optimized PyTorch experiments for distributed training, reducing training time by 28% and improving cluster utilization.
Skills
Python · PyTorch · Machine Learning · Deep Learning · Reinforcement Learning · LLMS · Model Evaluation · Rlhf · Distributed Training · Large Language Models · Transformers · Data Pipelines · Natural Language Processing · Software Engineering · SQL
More research scientist bullet points to adapt
- Built RLHF training loops and evaluation checks that improved instruction-following scores while keeping rejection rates stable.
- Designed distributed training experiments for large language models, lowering GPU idle time and improving throughput across runs.
- Created data pipelines that joined raw logs, annotations, and SQL reporting tables for consistent model training and analysis.
- Ran ablation studies on transformers and deep learning variants to identify the smallest model that met accuracy and latency targets.
- Implemented Python tooling for experiment tracking and model evaluation, making results reproducible across multiple researchers.
- Worked with software engineering partners to ship research models into production services with monitoring and rollback support.
- Extended reinforcement learning experiments to compare reward signals, improving benchmark performance on sequence generation tasks.
- Validated NLP model changes with slice-based evaluation, surfacing failure cases on rare intents and long-tail queries.
What sets an application apart
Listed as a plus rather than a requirement: Cuda (19%), Kubernetes (13%), C++ (10%), Docker (8%), NLP (8%), Observability (8%), Phd (8%) and Reinforcement Learning (8%). Worth naming if you have it, and never worth claiming if you do not.
Level, location and pay
- Most are senior roles (54% of postings that state a level).
- 25% are listed as remote.
- Listed mainly in United States, United Kingdom and France.
- US postings that publish a yearly salary: median $300k, middle half $300k–$415k (from 17 postings).