​A high-impact AI Researcher resume does two things fast: proves you can advance the state of the art, and shows you can ship rigorously evaluated results. Your resume is the first impression reviewers (and ATS) have of you, so make it reproducible, scannable, and outcome-driven. Use this guide to craft a resume that stands out for academic labs, industrial research teams, and applied research roles.

Lead with a crisp 3–4 line summary that names your focus area(s), signals research rigor, and quantifies impact (publications, benchmarks, citations, open-source traction).

AI Research Scientist — LLMs & Alignment

• Research scientist with 7+ years in NLP and generative models; first-author publications at top venues and multiple open-sourced LLM alignment methods.
• Proven record improving benchmark accuracy (+5–12 pts on MMLU, HellaSwag) and cutting inference latency 30–40% through distillation/quantization.
• Seeking to drive research that is safe, scalable, and product-relevant.

Applied ML → Research Transition

• Senior ML Engineer pivoting to research; led end-to-end systems from dataset curation to deployment for vision + language.
• Strong experimental design, ablations, and reproducibility; co-maintainer on popular PyTorch library (2k+ stars).
• Targeting an applied research team to translate new methods into products.

PhD Candidate / Recent Graduate

• PhD in CS, focus on multi-modal learning and efficient training; 3 first-author papers, 600+ citations, h-index 11.
• Designed novel data-efficient fine-tuning that reduces compute by 35% with parity accuracy.
• Eager to contribute to a research lab that values open science and rigor.

Research Manager / Lead

• Research lead managing 6 scientists/engineers; roadmap ownership from problem framing to publication and tech transfer.
• Delivered two SOTA results on public benchmarks and integrated findings into a product used by 5M+ MAU.

​Tip: Name your subfields (e.g., LLMs, RL, multimodal, robustness, safety), core toolset (PyTorch/JAX), and 2–3 quantifiable signals (citations, stars, SOTA deltas).

List highest degree first (PhD/MS/BS) with thesis title or topic if relevant to the role. Immediately underneath, add a compact Research Credentials line:

• Degrees: PhD/MS/BS (CS, EE, Math, Stats, Physics, or related). Include the thesis topic wherever helpful.
• Publications: Select top venues (e.g., NeurIPS, ICML, ICLR, ACL, CVPR, KDD). List 3–6 best; use consistent venue/year formatting.
• Preprints & Reviews: arXiv/tech reports; note if under review.
• Patents: Granted + notable filings.
• Scholar IDs: Google Scholar, ORCID, Semantic Scholar (links).
• Awards: Best paper/honorable mentions, fellowships, scholarships.
• Teaching/Service (optional): TA, reviewer area chair, workshop organizer.

Helpful Resources:

• Create/maintain profiles: Google Scholar, ORCID, and Semantic Scholar.
• arXiv & OpenReview for preprints and conference submissions.
• University career pages for CV→resume conversion guidelines.

​Present roles (research, internships, RAships, fellowships, applied roles) with integrated bullets that merge responsibility + achievement. Lead with the highest-impact outcomes.
 
Example (integrated bullets style):

• Designed a parameter-efficient fine-tuning method for LLMs (≤1% trainable params) that improved MMLU by +6.2 pts while cutting training compute –38% on A100s.
• Curated and de-biased a 120M-sample instruction dataset; increased factuality +9% (human eval) and reduced toxicity –22% (Perspective API).
• Open-sourced training pipeline; repo reached 3.4k stars, 300+ forks, 50+ external citations; adopted by two partner teams.
• Led ablation suite (20+ runs) isolating gains from data curriculum vs. optimizer tweaks; wrote reproducibility checklist and seed-control harness.
• Collaborated with product to ship distilled model to prod (p95 latency –41%, GPU cost –28%) without accuracy regression.

 

Pro Tip: Keep methods/results tightly paired. Every bullet should imply Problem → Approach → Evidence → Impact.

Use verbs that signal rigor, originality, and engineering depth:

• derived, formalized, proved, generalized
• designed, implemented, optimized, parallelized
• benchmarked, reproduced, validated, ablated
• fine-tuned, distilled, quantized, pruned
• curated, annotated, augmented, de-biased
• evaluated, audited, stress-tested, red-teamed
• authored, published, open-sourced, maintained
• mentored, led, coordinated, collaborated

Core Research & Math

• Probability & statistics, linear algebra, optimization, information theory
• Experimental design, causal inference (where relevant), scientific writing

 

Machine Learning / Deep Learning

• Foundation models (LLMs, vision transformers), diffusion/generative models
• Representation learning, RL/RLHF, retrieval-augmented generation
• Prompting/finetuning (LoRA/QLoRA), alignment/safety, evaluation methods

 

Programming & Tools

• PyTorch / JAX / TensorFlow; CUDA basics; NumPy, Pandas
• Training at scale (distributed data/model parallelism), mixed precision
• Experiment tracking (Weights & Biases/MLflow), profiling, and debugging

 

Data & MLOps

• Data pipelines, dataset governance & documentation (datasheets/model cards)
• Model serving (Triton, ONNX), A/B testing, monitoring, and rollback strategies
• Security, privacy, safety evaluations; red-teaming

 

Communication & Leadership

• Technical writing (papers, docs), research talks, collaboration across product/legal/policy
• Mentoring interns, code reviews, project scoping, and roadmapping

Numbers are your evidence. Prioritize benchmark deltas, compute efficiency, adoption, citations, and product impact.
 
Examples:

• Achieved state-of-the-art on MultiNLI with +2.1 pts over prior SOTA using retrieval-augmented fine-tuning on 64×A100 (training time –30%).
• Distilled 13B → 3B model with 0.7-point average loss in accuracy across 8 tasks; reduced p95 latency –45% and memory –52%.
• Led release of a safety evaluation suite (toxicity, jailbreaks); reduced prompt-induced unsafe outputs –35% after mitigations.
• First-author ICLR 2025 (oral); 1,200+ citations total; h-index 14 (Google Scholar).
• Open-sourced library adopted by 5+ external teams; 4.1k stars, 200k monthly downloads.

 

Helpful Resource:

• How to Quantify Your Accomplishments on Your Resume

​Make it easy to verify your work.
 

• Publications (selected): Author list • Title • Venue, Year • Link/DOI • One-line contribution/impact.
• Open Source: Repo name • Role (author/maintainer) • Notable features • Stars/forks/downloads.
• Models & Datasets: Hugging Face links • Model card highlights • License • Usage stats.
• Talks & Tutorials: Title • Event • Link • Audience size/ratings if notable.

 

Tip: Keep this section lean on a resume (not a CV). Link to a full publications page.

• Clean layout, consistent headings, and compact bullets (1–2 lines).
• Reverse-chronological; 1–2 pages for industry; links in text (GitHub, Scholar, HF).
• Use standard section names (Summary, Experience, Education, Skills, Publications).
• Export to PDF unless the employer requests DOCX.
• Proofread meticulously; verify every link; ensure reproducibility claims are real.

Mirror the job’s language and emphasize the most relevant work:
 

• If the role is LLM Safety, foreground red-teaming, evals, alignment, policy collaboration.
• If it’s Efficient Training/Serving, highlight distillation, quantization, compilers (TorchDynamo, XLA), kernel-level wins.
• If it’s Multimodal, lead with vision-language datasets, cross-modal attention, and retrieval pipelines.
• Re-order skills and bullets by relevance; reuse the job’s exact keywords for ATS.

 

Helpful Resources:

• Customize Your Resume
• How to Customize Your Resume

• Advanced method → result: Proposed [method] for [task], yielding [metric +Δ/–Δ] on [benchmark] with [compute/resource change].
• System + adoption: Built [library/pipeline] enabling [capability]; reached [stars/downloads/adopters] and [impact].
• Data → quality: Curated [dataset size/type] with [governance step]; improved [factuality/robustness] by [value].
• Productization: Deployed [model] to [env]; reduced [latency/cost] [value] while maintaining [metric].
• Leadership: Mentored [#] interns; resulted in [paper/tool] and [award/adoption].

Use the cover letter to connect your research interests with the team’s roadmap, summarize 1–2 flagship results (with links), and state how you’ll contribute in the first 90 days (e.g., “replicate + extend paper X; productionize method Y”)

​Helpful Resource:
How to Write a Cover Letter

• Name • Title (AI Research Scientist | NLP/LLMs)
  Email • Location • GitHub • Google Scholar • Hugging Face • LinkedIn • Website
• Summary (3–4 lines with focus areas + quantified signals)
• Experience (integrated bullets; 4–6 most relevant wins first)
• Education (degree, thesis, advisor if helpful)
• Skills (grouped: Research | ML/DL | Tools | Data/MLOps | Communication)
• Research Footprint (selected pubs/models/repos)
• Awards/Service (concise)

• Summary names focus areas + 2–3 metrics
• Bullets use Problem → Approach → Evidence → Impact
• Benchmarks & ablations quantified
• Links to code/models/papers provided
• Format is clean, 1–2 pages, ATS-friendly
• Resume tailored to the posting

​You’ve got this. With sharp evidence, clear writing, and links that prove your claims, your resume will read like a well-run experiment, and win the review.

About the Author

Mandy Fard is a Certified Professional Resume Writer (CPRW, CMRW) and Recruiter with decades of experience in assisting job seekers, working directly with employers in multiple industries, and writing proven-effective resumes.
 
Feel free to connect with Mandy Fard on LinkedIn: https://www.linkedin.com/in/mandyfard/
 
Please follow Market-Connections Resume Services on LinkedIn:
https://www.linkedin.com/company/market-connections-resume-services.com