Real, ATS-tested resume examples for ML, AI, LLM, MLOps, and research roles - written by people who hire for them. Free to copy and customise.
AI hiring managers are reading for a specific signal, and it is not the one most candidates put on the page. They have read a thousand resumes that say "built ML pipelines using TensorFlow and AWS." They are not looking for that. They are looking for evidence that you can take a model from a notebook to something that earns its keep against a real eval set, on a real budget, in a real product.
That changes what gets weight on your resume. A bullet about a fine-tuning run matters because of the eval delta and the cost - not because you used LoRA. A retrieval pipeline matters because of recall@k on a golden set and p95 latency under load - not because you wired up a vector DB. A research project matters because of the result it landed and where it landed - not because you ran a transformer.
"Hiring managers don't read your skills section. They read your bullets and decide if your skills section is bullshit."
The framework names matter, but only the right ones, and only in context. Listing PyTorch, JAX, vLLM, Triton, Ray, or a specific quantisation library is useful when the bullet around it shows you used it under pressure - shaved 40% off inference cost, kept p95 under 800ms during a launch, replaced a brittle prompt with a fine-tune that improved factuality 18 points on a held-out set. Names without numbers read as a buzzword pile.
Senior roles add one more bar: external signal. A first-author paper at a top venue, a maintained open-source repo with real users, a talk at a serious conference, or a clearly written technical blog. For research roles this is table stakes. For senior engineering roles it is increasingly the tiebreaker between two strong on-paper candidates. If you have it, it goes on page one.
"The candidates who get AI offers are comfortable being a researcher on Tuesday and an engineer on Wednesday. Your resume needs to show both."
The other thing AI hiring managers screen for is range. Pure researchers who cannot deploy get filtered out of product teams. Pure engineers who cannot read a paper get filtered out of model teams. The candidates who get offers are comfortable being a researcher on Tuesday and an engineer on Wednesday - and their resumes show both. One bullet about an eval methodology you designed. One bullet about a production rollout you owned. One about cost or latency you fixed. That is the shape that converts.
Each example shows the bullet structure, ATS keywords, and metrics hiring managers look for in that specific AI/ML role.
You ship models that earn their keep in prod.
Ship LLM-powered features that work in production - not just in demos.
AI engineer resume example with LLM integration, RAG pipeline, and evaluation bullets. Shows hiring managers …
Take models from notebook to production without cutting corners.
Machine learning engineer resume example with model deployment, MLOps, and inference performance bullets. Tai…
Production ML platforms that train, deploy, and monitor without the 3am pages.
MLOps engineer resume example with model registry, feature store, and drift monitoring bullets. The keywords …
Prompts, RAG, fine-tuning, and the eval rigor that keeps models honest.
LLM engineer resume example with retrieval pipeline, fine-tuning, and evaluation bullets. The exact keywords …
Diffusion, multi-modal, and agentic systems shipping to real users.
Generative AI engineer resume example covering diffusion models, multi-modal pipelines, and agent orchestrati…
You find the signal first, then engineers ship it.
Publications, novel methods, and the labs hiring at NeurIPS deadline.
Research scientist resume example with first-author papers, citations, and benchmark wins. Tuned for FAIR, De…
Research depth, production discipline, and shipped impact in one resume.
Applied scientist resume example with model improvement metrics, A/B test wins, and the publication signals A…
Models, experiments, and insights that move the business forward.
Data scientist resume example with ML model results, A/B test wins, and the Python/SQL keywords ATS systems l…
Deep stack in one modality - vision, language, or prompts.
Detection, segmentation, and on-device inference that ships at frame rate.
Computer vision engineer resume example with model accuracy, edge deployment, and CUDA optimisation bullets. …
From classical text pipelines to transformer-era language systems.
NLP engineer resume example with classification, NER, and LLM-augmented retrieval bullets. The keywords AI-he…
Eval-driven prompts that hold up in production - not vibes-based one-shots.
Prompt engineer resume example with evaluation harnesses, accuracy lifts, and cost reductions. The role has m…
First-pass screens are fast. These are the things that get a "yes, schedule them" before anyone reads paragraph two.
Latency (p50/p95/p99), throughput (req/s, tokens/s), $/inference, accuracy lifts on a named eval. "Reduced p95 latency 38% on the recommendations endpoint, saving $14k/month in GPU spend" beats "optimised model performance" every time.
Golden sets you built or curated. A/B tests you ran. Regression tests that caught a real prompt drift. Naming the eval methodology - LLM-as-judge with calibration, human-rated rubric, MMLU/HELM-style harness - signals you take quality seriously, not just vibes.
Read the JD's stack and mirror it. Hiring at a vLLM shop? "Migrated inference from HF Transformers to vLLM, 3.4x throughput at same latency" lands. PyTorch, JAX, Ray, Triton, Modal, Anyscale, LangGraph, DSPy - name the ones the company actually runs.
Research roles: papers (with venues) and a clean repo. Engineering roles: a deployed model behind a real product surface. AI engineer roles: prompt + eval discipline (versioning, regression suites, structured outputs). Match the proof to the job.
Recruiters look for a story: SWE -> ML engineer pivot, PhD -> applied scientist, researcher -> AI engineer at a startup. Make the arc obvious in your summary line and your most recent role. A one-line "previously: backend at Stripe" or "PhD in computational neuroscience, now shipping LLM products" reframes the rest of your resume in one breath.
28 ATS-friendly templates, 14 of them free. AI tailoring on every job. Free to start, cancel anytime.
