Lead with impact, not model names
Many ML resumes read like a list of architectures: “Built a transformer model,” “Used XGBoost.” Hiring managers want to know what changed because of your work, and whether you can tell a real improvement from a lucky benchmark.
Use the STAR format (Situation, Task, Action, Result) compressed into one bullet. Instead of “Worked on the search ranking model,” write: “Replaced keyword ranking with a fine-tuned embedding model, raising click-through on search results by 12% in an A/B test and cutting serving cost by 30% with int8 quantization.”
Tailor your skills section
The skills section is scanned first by applicant tracking software and then by the engineers who run your technical screen. List what you can be interviewed on, not every library you have imported.
- Languages: Python first, then SQL, C++, CUDA or Rust if you use them
- ML frameworks: PyTorch, JAX, TensorFlow, scikit-learn, Hugging Face Transformers
- LLM and GenAI work: fine-tuning methods, RAG, vector databases, evaluation frameworks
- MLOps and infrastructure: experiment tracking, model serving, Kubernetes, a cloud platform
- Leave out soft skills; show them in your bullets instead
Quantify the right things
A model metric on its own means little to a reader who does not know the baseline or the dataset. Give the comparison (“from 0.71 to 0.83 F1 against the production model”) and, where you can, the effect on the product or the business. If you only have estimates, mark them with “~”.
Metrics that land well:
Model quality against a stated baseline (accuracy, F1, eval pass rate)
Production results (A/B test lift, fewer errors or escalations)
Efficiency (inference latency, GPU hours, cost per request)
Scale (training data size, requests served, models in production)
Projects, papers and code
In AI and ML, public work carries real weight. Link to it directly from your resume, and make each link worth clicking.
- Papers: list them in a Publications section with venue and year; put your name in bold
- GitHub: pin two or three repos with a README that explains the problem and the results
- Projects: one end-to-end project with an honest evaluation beats five tutorial notebooks
- Competitions and open source: say what you placed or what your contribution changed
ATS tips
Applicant tracking systems parse your resume before a person reads it. A designed layout that confuses the parser can be filtered out unseen.
- Use standard headings: Experience, Education, Publications, Skills, Projects
- Avoid tables, columns, text boxes and graphics
- Submit a PDF unless the application asks for .docx
- Use the job description's own terms (“LLM evaluation,” not just “evals”)
On TensorHunt, your match score is calculated from your profile (headline, bio, skills, experience and education), not from your uploaded resume file. Copy your strongest bullets and skills into your profile too.
The format matters
One page for under 8 years of experience; two pages beyond that. Researchers with a long publication list can add it as a separate page or link to Google Scholar. Use a clean font at 10–11pt and left-align everything. No photos, no icons, no sidebars.
The best resumes look a little boring by design. The person reading yours wants to find the results in seconds.