Research Engineer resume skills and keywords
In 240 Research Engineer job postings from 128 employers analysed on October 23, 2026, the most requested skills are Python (60 %), PyTorch (45 %), Machine learning (39 %), Communication (38 %) and LLMs (37 %). Of the top 40 skills, 34 are hard skills, tools or certifications and 6 are soft skills.
Download the full list as a CSV or check your own resume against a posting with the free keyword scanner.
Top 40 skills in Research Engineer job postings
| Skill | Share of postings | Type |
|---|---|---|
| Python | 60 % | Tool |
| PyTorch | 45 % | Tool |
| Machine learning | 39 % | Hard skill |
| Communication | 38 % | Soft skill |
| LLMs | 37 % | Tool |
| Publication | 29 % | Hard skill |
| Algorithms | 21 % | Hard skill |
| Collaboration | 20 % | Soft skill |
| Deep learning | 19 % | Hard skill |
| Distributed training | 18 % | Hard skill |
| Data pipelines | 17 % | Hard skill |
| Experimentation | 17 % | Hard skill |
| Robotics | 17 % | Hard skill |
| Logistics | 16 % | Hard skill |
| APIs | 15 % | Hard skill |
| Debugging | 15 % | Hard skill |
| Kubernetes | 12 % | Tool |
| Distributed systems | 12 % | Hard skill |
| Artificial Intelligence | 12 % | Hard skill |
| Computer vision | 11 % | Hard skill |
| Data Processing | 11 % | Hard skill |
| Model evaluation | 9 % | Hard skill |
| Docker | 9 % | Tool |
| Tool use | 9 % | Tool |
| Evaluation frameworks | 8 % | Tool |
| C++ | 8 % | Tool |
| TensorFlow | 7 % | Tool |
| Problem Solving | 7 % | Soft skill |
| Creativity | 7 % | Soft skill |
| Flexibility | 7 % | Soft skill |
| Quantization | 7 % | Hard skill |
| Natural language processing | 7 % | Hard skill |
| Linux | 7 % | Tool |
| PhD in Computer Science | 7 % | Hard skill |
| Generative AI | 6 % | Tool |
| Prototyping | 6 % | Hard skill |
| Token | 6 % | Hard skill |
| Performance optimization | 6 % | Hard skill |
| Attention to detail | 5 % | Soft skill |
| AI Tools | 5 % | Tool |
Hard skills vs soft skills vs tools and certifications
Hard skills (22)
- Machine learning 39 %
- Publication 29 %
- Algorithms 21 %
- Deep learning 19 %
- Distributed training 18 %
- Data pipelines 17 %
- Experimentation 17 %
- Robotics 17 %
- Logistics 16 %
- APIs 15 %
- Debugging 15 %
- Distributed systems 12 %
- Artificial Intelligence 12 %
- Computer vision 11 %
- Data Processing 11 %
- Model evaluation 9 %
- Quantization 7 %
- Natural language processing 7 %
- PhD in Computer Science 7 %
- Prototyping 6 %
- Token 6 %
- Performance optimization 6 %
Soft skills (6)
- Communication 38 %
- Collaboration 20 %
- Problem Solving 7 %
- Creativity 7 %
- Flexibility 7 %
- Attention to detail 5 %
Tools and software (12)
- Python 60 %
- PyTorch 45 %
- LLMs 37 %
- Kubernetes 12 %
- Docker 9 %
- Tool use 9 %
- Evaluation frameworks 8 %
- C++ 8 %
- TensorFlow 7 %
- Linux 7 %
- Generative AI 6 %
- AI Tools 5 %
Keyword phrases recruiters use
Multi-word phrases that recur across these research engineer postings, with the share of postings that use each one.
How to use this on your resume
Treat the table as a checklist, not a script. Put the skills you genuinely have in your skills section, using the same wording as the posting, and back the top ones with a bullet that shows the result.
Each job posting differs from the average, so compare your resume with the actual ad: paste it into the resume keyword scanner to see which terms you already have and which are missing. The ATS keywords guide explains where to place them so applicant tracking systems read them.
Skip any skill you cannot talk about in an interview; a keyword that falls apart under one question costs more than it gains. For a full layout with these skills in context, see the Research Engineer resume example. Then prepare for the Research Engineer interview questions that test them.
Build a research engineer resume with these skills
Start in the editor, tailor your bullets to the job you want and download an ATS-friendly resume.