Machine Learning Engineer (MLE)
Resume Template

A free Machine Learning Engineer (MLE) resume, pre-filled and ready to edit. Replace the highlighted placeholders (frameworks, serving infrastructure, MLOps tools, evaluation metrics) using the side panel on the left, and the resume rewrites itself as you type. Save as PDF when you're done.

Emmanuel Gendre - Former Google Recruiter and Tech Resume Writer

Authored by

Emmanuel Gendre

Tech Resume Writer

Last updated: May 8th, 2026 | Free, ATS-friendly, no signup

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Interactive resume template generator

Interactive ML Engineer Resume Template

Edit the side panel. The resume rewrites itself live. Save as PDF when you're done.

Edits update live as you type. Toggle Edit to rewrite paper text directly.

Edit mode is on. Click anywhere on the resume to rewrite text. Side-panel placeholders still update live.

Nikhil Rao Machine Learning Engineer

Mountain View, CA • ml@gmail.com • +1 6505-2222

Profile Summary

  • Machine Learning Engineer with 6 years of experience designing and operating production ML systems across LLM safety, content recommendations, and ranking systems, specializing in model training, low-latency serving, and MLOps.
  • Solid technical background across frameworks (PyTorch, TensorFlow, Hugging Face), languages (Python, SQL), serving infrastructure (Triton, KServe), MLOps (MLflow, Weights & Biases, Feast), and cloud (AWS, GCP) with strong fundamentals in distributed training and GPU optimization.
  • Deep expertise in end-to-end ML system design, LLM fine-tuning, real-time model serving, and responsible AI evaluation, leveraging methodologies such as continuous training pipelines and shadow deployments to drive reliable, observable, and cost-aware ML platforms.
  • Engaged collaborator working cross-functionally with Research, Product, and Eng teams in Agile environments, contributing to model-launch reviews, evaluation design, and post-launch retrospectives with a pragmatic, ownership-first mindset.
  • Emerging leader who shares technical excellence and fosters a culture of rigor in evaluation and reproducibility discipline through PR reviews and runbooks, while leading ML guild sessions and authoring widely adopted training-pipeline templates.

Technical Skills

ML Frameworks & Libraries:
PyTorch, TensorFlow, JAX, scikit-learn, Hugging Face, vLLM
Languages & Scripting:
Python, SQL, Go, C++, Bash
Data & ETL for ML:
Spark, Ray, Apache Beam, Pandas, dbt, Airflow
Feature Stores:
Feast, Tecton, Vertex AI Feature Store
Model Serving:
TorchServe, Triton, KServe, SageMaker, Vertex AI
MLOps & Experiment Tracking:
MLflow, Weights & Biases, Kubeflow, Metaflow, DVC
Cloud & Compute:
AWS (SageMaker, S3, EKS, Lambda), GCP (Vertex AI, GKE), GPU/TPU clusters
Evaluation & Responsible AI:
Offline/online evals, A/B testing, fairness audits, robustness checks

Education

Stanford University M.S. in Computer Science (ML focus)
Stanford, CA • Sep 2018 - Jun 2020

Work Experience

Anthropic Senior Machine Learning Engineer
San Francisco, CA • Sep 2022 - Present
  • Owned end-to-end ML system architecture for the Claude evaluation platform processing 20M+ evaluation runs per month, leading design across training pipelines, serving infrastructure, and feedback loops spanning 8 model families in a polyglot Python, Go, and Rust environment.
  • Trained and fine-tuned a safety classifier for constitutional AI rejections using PyTorch and Hugging Face, anchored on LoRA fine-tuning with DPO post-training and gradient checkpointing; lifted refusal precision from 84% to 96% on the internal redteam benchmark.
  • Deployed models as real-time inference APIs on Triton and KServe with dynamic batching, model parallelism, and token-level streaming, serving 35k QPS at 320ms p95 latency and 99.95% uptime across multiple regions.
  • Built the team's model training and release pipeline in MLflow with dataset versioning via DVC, experiment tracking, and automated eval gates; cut model lead time from commit to prod from 4 weeks to 3 days.
  • Stood up production model monitoring for 8 models in serving, tracking input drift via population-stability index, output distribution shifts, and business KPI tracking; surfaced 14 silent regressions in the first six months and triggered 4 emergency retrains.
  • Optimized inference cost through INT8 quantization, knowledge distillation, and GPU utilization batching, lifting throughput by 3.2x (from 11k QPS to 35k QPS) and cutting per-token serving cost by 62% during a major scale-up.
  • Designed the team's offline + online evaluation framework around A/B-tested capability evals, shadow-deployed safety probes, and bias-and-fairness audits, running 60+ structured evals that gated 9 model launches without a customer-visible regression.
Meta Machine Learning Engineer
Menlo Park, CA • Aug 2020 - Aug 2022
  • Built 180+ production features for the Reels ranking model, owned through a Feast feature store with point-in-time correctness, freshness monitoring, and shared training/serving paths, powering 6 ranking models and lifting top-line engagement by 8%.
  • Owned training data pipelines in Spark on EMR and Apache Beam, processing 50TB/day of interaction logs with schema enforcement, dedup and quality checks, and lineage tracking, hitting a 2-hour freshness SLA across batch and streaming inference paths.
  • Implemented a two-tower retrieval model for content recommendations in TensorFlow, training on 2B+ user interactions across 4xA100 GPUs, lifting NDCG@10 by 14.5% vs the previous Wide&Deep baseline.
  • Worked closely with Product, Eng, and Trust & Safety teams across 3 product surfaces to negotiate evaluation criteria, metric definitions, and launch gates, authoring 7 ML RFCs that shaped the org's responsible-AI guardrails and onboarding 10 new MLEs.

Done editing? Download as a real, vector PDF. Selectable text, ATS-friendly, US Letter format.

About this template

A Machine Learning Engineer (MLE)
Resume Template, by an Engineering CV Service.

Backstory: 14 years recruiting in tech, plus several at Google. I now run an engineering resume service only for engineers and IT folks, and ML Engineer rewrites come through every week. So when I describe what hiring teams on competitive ML orgs scan for in those first few seconds, that's the recruiter's view, not the candidate's.

Most clients hire me for the full custom rewrite. We pull out the actual systems you shipped, the models that moved a metric, the cost or latency wins worth highlighting. Sometimes that's overkill, though. If a strong skeleton with ML-shaped placeholders is enough, this template is exactly that. ATS-clean, free, no signup. Have a go.

How it works

How to use this template
to write a Machine Learning Engineer (MLE) resume

The structure here was written by a former Google recruiter. The placeholders force you to be specific exactly where it matters: frameworks, services, ML architecture, and metrics.

Strong ML Engineer resume bullets aren't written in a single pass. They build through five stages. Stage one names the task. Stages two and three add the frameworks you used and the services that ran them. Stage four shows the ML system decision behind the work. Stage five quantifies the result. Bullets that complete stage five are the ones a hiring manager flags for the phone screen. The complete framework lives in How to Write Bullet Points for Tech Resumes.

  1. 01 Task What you did
  2. 02 Frameworks PyTorch, TF, JAX
  3. 03 Services Triton, MLflow
  4. 04 Architecture How you designed
  5. 05 Metric Quantified impact

This template hard-wires the five stages into your bullets so the framework runs in the background. The side panel maps clean: framework and language picks fill stage 2, serving and MLOps picks fill stage 3, the architecture-pattern fields fill stage 4, the metric inputs land at stage 5. The sentence skeletons cover stage 1. Why this matters: you only need to drop in real tools and real numbers. The structure handles the rest, and the resume reads at stage 5.

  1. Pick your stack

    Tap a chip to swap PyTorch for TensorFlow or JAX, Hugging Face for native PyTorch, MLflow for W&B, Triton for SageMaker. Every mention updates at once.

  2. Drop in your numbers

    Model accuracy lift, QPS, p95 latency, training cost, drift incidents, evaluation count. Don't have yours yet? The defaults pass for a senior MLE resume.

  3. Save as PDF

    Click Download. The page generates a real vector PDF with selectable text and clean US Letter formatting. ATS-parsable.

Resume Sample

ML Engineer Resume Examples

Three sample ML engineer resumes at different career stages: a junior research-to-production pivot at a foundation-model lab, a senior IC on a gaming-recommendation team, and a staff ML lead at a GPU and foundation-model company. Use them as inspiration when filling the template above.

Entry-level ML Engineer Resume Sample 2 years

Junior ML Engineer Resume Example

Research-to-production pivot. Owns 2 training pipelines and contributes to retrieval evaluation at a foundation-model lab.

Diego Velasquez

Junior ML Engineer

Toronto, ON · diego.velasquez@gmail.com · +1 416-555-0137 · linkedin.com/in/diegovelasquez

Profile Summary
  • Junior ML Engineer with 2 years of experience moving research prototypes into production at a foundation-model lab, with a focus on training pipelines, retrieval evaluation, and reproducible experimentation.
  • Hands-on coverage across Python, PyTorch (basic Lightning), HuggingFace Transformers, scikit-learn, Weights & Biases, and FastAPI, with foundational knowledge of Ray, Docker, and AWS SageMaker.
  • MSc in Computer Science with a thesis on dense retrieval for long-context QA, bringing peer-reviewed evaluation rigor to production model work and a comfort with reading recent ML papers.
  • Collaborative engineer working with research scientists, senior MLEs, and platform teams under senior mentorship, contributing to 2 training pipelines and 3 retrieval evaluation pipelines in the first 18 months on the job.
Technical Skills
Languages & Core:
Python, basic SQL, Git, pandas, NumPy, scikit-learn
Deep Learning:
PyTorch, basic PyTorch Lightning, HuggingFace Transformers, HuggingFace datasets
Distributed & Tracking:
Basic Ray, Weights & Biases, basic distributed data loading
Serving & Infra:
FastAPI, basic Docker, basic AWS S3, basic AWS SageMaker
Evaluation:
Retrieval evaluation (recall@k, MRR, nDCG), unit tests for data preprocessing
Languages:
Python, basic SQL, basic Bash
Education
University of Toronto M.Sc. in Computer Science Toronto, ON · Sep 2020 - Jun 2022
University of British Columbia B.Sc. in Computer Science Vancouver, BC · Sep 2016 - May 2020
Work Experience
Cohere Junior ML Engineer Toronto, ON · Sep 2023 - Present
  • Built and maintained 2 training pipelines in PyTorch Lightning for small-scale fine-tuning runs on retrieval embedding models, including data loaders, checkpoint management, and Weights & Biases logging.
  • Contributed 6 small evaluation jobs to the internal eval harness for retrieval and reranker models, owning recall@k and nDCG dashboards reviewed weekly by 2 senior MLEs.
  • Helped build 3 retrieval evaluation pipelines on top of HuggingFace datasets, covering long-context QA, multilingual recall, and citation-grounding sanity checks.
  • Authored unit tests for data preprocessing across 4 tokenization paths in pytest, catching 2 silent encoding bugs before they reached training runs.
  • Contributed to a model-card automation script under senior mentorship, cutting per-release model-card prep from 4 hours to under 30 minutes.
Layer 6 AI Research Intern, then Junior MLE Toronto, ON · Jun 2022 - Aug 2023
  • Reproduced 4 published baselines for sequential recommendation in PyTorch, including data loaders, training loops, and offline evaluation on MovieLens-25M and proprietary banking sequences.
  • Built a scikit-learn baseline harness used by 3 research scientists as a quick sanity check before launching deep-learning experiments.
  • Contributed to a FastAPI inference endpoint wrapping a transformer-based ranker, including basic input validation and a small Docker container deployed to a staging EKS cluster.

Senior ML Engineer Resume Sample 7 years

Senior ML Engineer Resume Example

Senior IC on a gaming-recommendation team. Owns the homepage candidate-generation model and online experimentation.

Naomi Tanaka

Senior ML Engineer

San Mateo, CA · naomi.tanaka@gmail.com · +1 650-555-0164 · linkedin.com/in/naomitanaka

Profile Summary
  • Senior ML Engineer with 7 years of experience building production recommendation and ranking systems at consumer gaming and AR scale, specializing in candidate generation, two-tower retrieval, and online experimentation.
  • Hands-on coverage across Python, PyTorch, JAX, Ray, Spark, FAISS, ScaNN, MLflow, Vertex AI, Kubeflow Pipelines, and Feast, with deep fundamentals in Two-Tower and DLRM patterns.
  • Owns 3 production models end-to-end: training pipeline, offline eval, online A/B test design, monitoring, and on-call rotation for the homepage candidate generator and 2 ranking surfaces.
  • Cross-functional engineer working with Product, Backend, and Data Platform teams on RFC authorship, capacity planning, and quarterly experimentation roadmaps in continuous-delivery environments.
  • Emerging tech lead, mentoring 3 mid-level engineers, running the team's weekly experiment-review forum, and authoring 4 RFCs adopted by the recommendations platform team.
Technical Skills
Languages:
Python, Go (services), SQL, basic C++, Bash
Deep Learning:
PyTorch, JAX, HuggingFace Transformers, Two-Tower, DLRM, sequence models
Distributed & Data:
Ray, Spark, Dataflow, BigQuery, Beam, parquet, Feast feature store
Retrieval & Ranking:
FAISS, ScaNN, hybrid retrieval, candidate generation, ranking pipelines
ML Platform:
MLflow, Vertex AI, Kubeflow Pipelines, GCP (BigQuery, Dataflow, Vertex AI), GitHub Actions
Serving & APIs:
gRPC inference serving, FastAPI, Docker, Kubernetes (basic), batch + online inference
Experimentation:
Online A/B test design, multi-armed bandits, CUPED, sequential testing, holdout audits
Observability:
Prometheus, Grafana, model-drift monitoring, training-eval skew alerts
Education
University of California, Los Angeles B.S. in Computer Science Los Angeles, CA · Sep 2014 - Jun 2018
Work Experience
Roblox Senior ML Engineer San Mateo, CA · Aug 2022 - Present
  • Own the homepage candidate-generation model, a two-tower retrieval system over 40M+ experiences serving 70M+ daily active users, with full responsibility for training, evaluation, online rollout, and on-call.
  • Drove a two-tower retrieval rewrite from a legacy matrix-factorization baseline, lifting session-level relevance by 32% and homepage CTR by 11% in a 4-week A/B test.
  • Designed and shipped 10 online experiments per quarter, including 3 holdout audits, partnering with Product on experiment guardrails and the team's quarterly experimentation calendar.
  • Built a FAISS + ScaNN hybrid retrieval layer serving 18k QPS at p99 latency under 35ms, with online index updates every 30 minutes.
  • Authored 4 RFCs adopted by the recommendations platform team, including the Feast feature-store rollout and the cross-team experiment-metadata standard.
  • Mentor 3 mid-level ML engineers through 1:1s and RFC reviews; chair the weekly experiment-review forum reviewed by 2 staff engineers.
  • Cut training-eval skew incidents from 8 per quarter to 1 by adding training-time feature snapshots and a feature-store consistency CI check.
Niantic ML Engineer San Francisco, CA · Jul 2018 - Jul 2022
  • Built and shipped 5 ranking models for the Pokemon GO sponsored-location surface, owning training pipelines on Kubeflow Pipelines with MLflow tracking.
  • Owned the player-segmentation feature pipeline on Spark + BigQuery, processing 4B+ events/day for 12 downstream consumers.
  • Designed and shipped 22 online A/B tests on the sponsored-location surface, contributing to a 14% revenue lift for the partner monetization program.
  • Migrated 3 legacy TensorFlow models to PyTorch, cutting training time by 40% and unblocking the team's Ray-based distributed training rollout.

Lead ML Engineer Resume Sample 11 years

Staff ML Engineer Resume Example

Staff ML lead at a GPU and foundation-model company. Manages 7 ML engineers and the inference-stack roadmap.

Adrián Schmidt

Staff ML Engineer

Santa Clara, CA · adrian.schmidt@gmail.com · +1 408-555-0119 · linkedin.com/in/adrianschmidt

Profile Summary
  • Staff ML Engineer with 11 years of experience leading large-scale training and inference programs across GPU, autonomous-vehicle, and foundation-model workloads, specializing in distributed training, inference-runtime performance, and ML platform governance.
  • Hands-on coverage across Python, C++ (CUDA kernels intro), PyTorch (DDP, FSDP), Triton, TensorRT-LLM, vLLM, NVIDIA Megatron-LM, Ray, DeepSpeed, and Kubernetes (GKE) with the NVIDIA GPU operator.
  • Deep expertise in multi-node H100 training, Horovod and NCCL-based collective communication, TensorRT-LLM inference optimization, and FSDP migration from legacy DDP at trillion-parameter scale.
  • Cross-functional leader partnering with Research, Product, Hardware, and Finance teams on inference-cost roadmaps, capacity planning, and executive cost-of-inference briefings.
  • Tech-lead managing 7 ML engineers, owning the inference-stack roadmap, RFC governance, and the on-call rotation for high-stakes inference services.
Technical Skills
Languages:
Python, C++ (CUDA kernels intro), Bash, basic Rust
Distributed Training:
PyTorch DDP and FSDP, Horovod, NCCL, multi-node H100, NVIDIA Megatron-LM, DeepSpeed
Inference Runtime:
Triton Inference Server, TensorRT-LLM, vLLM, batched and continuous batching, paged attention
Orchestration:
Kubernetes (GKE), NVIDIA GPU operator, Ray, Slurm, Argo Workflows
ML Platform:
MLflow, Weights & Biases, ML platform RFC governance, on-call rotation playbooks
Observability:
Prometheus, Grafana, GPU metrics (DCGM), inference SLO dashboards, cost-of-inference reporting
Leadership:
RFC processes, ML platform governance, hiring loops, executive cost-of-inference briefings
Cloud:
GCP (GKE, Vertex AI), AWS (EKS, S3), on-prem GPU clusters
Education
ETH Zurich M.S. in Computer Science Zurich, Switzerland · Sep 2012 - Jun 2014
Universität Hamburg B.Sc. in Computer Science Hamburg, Germany · Sep 2009 - Jul 2012
Work Experience
NVIDIA Staff ML Engineer Santa Clara, CA · Mar 2021 - Present
  • Tech lead for the inference-runtime team, managing 7 ML engineers and owning the Triton + TensorRT-LLM stack that serves 6 production models at 22k QPS across internal and partner workloads.
  • Led the FSDP migration from legacy DDP across 4 training programs, unlocking multi-node H100 training at trillion-parameter scale and cutting per-step time by 34%.
  • Drove a TensorRT-LLM inference-optimization program across 3 flagship models, lifting throughput by 52% and cutting p99 latency by 38% through paged attention, continuous batching, and KV-cache tuning.
  • Defined the team's RFC governance process, shepherding 18 RFCs through review and adoption; chair the bi-weekly Inference Platform forum.
  • Owns the on-call rotation for 4 high-stakes inference services, including runbook authorship, post-incident reviews, and SLO budget enforcement.
  • Briefs executive leadership on quarterly cost-of-inference budgets, including GPU capacity planning and 6-quarter inference-cost projections.
  • Mentor 4 senior engineers through staff-engineer trajectory; led 8 internal architecture reviews and authored the inference-runtime onboarding curriculum.
Cruise Senior ML Engineer San Francisco, CA · Jul 2014 - Feb 2021
  • Owned the perception model training platform, building Horovod-based distributed training pipelines for 12 perception models across LiDAR, camera, and radar modalities.
  • Built the inference runtime on a custom GPU operator, serving 8 perception models on-vehicle with p99 latency under 25ms.
  • Led the Kubernetes-based training cluster migration across 3 GPU pools, unlocking 4x training-throughput growth and cutting per-experiment cost by 42%.
  • Authored 9 RFCs across training platform, data versioning, and on-vehicle inference; adopted across the perception org.
  • Mentored 6 mid-level and senior engineers, ran the weekly platform craft session, and contributed to 5 hiring loops as a senior interviewer.

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Frequently asked

Your Questions about the Machine Learning Engineer (MLE) Resume Template, Answered

Yes, no charge. No signup, no email required, no upgrade tier in the wings. Open it, fill in your details, save the PDF, and you're done.

Yes. The exported PDF is single-column with the section headers ATS systems read by default (Profile Summary, Technical Skills, Education, Work Experience), no tables, no images, no multi-column layouts. Workday, Greenhouse, and iCIMS handle it cleanly. Drop the export into our ATS Checker after if you want a second look.

You can. Toggle Edit at the top of the resume preview, then click into any sentence and rewrite it directly. The side-panel placeholders keep updating; the rest of the text is plain editable copy.

Hit Download. Your browser generates the PDF on the spot, no print dialog, no signup, no server in the loop. The output is real vector text on US Letter, parsed by ATS systems the same way they parse any clean resume export.

Yes. The defaults lean PyTorch + Hugging Face + LLM tooling because that's where 2026 ML Engineer JDs concentrate, but every reference is a placeholder. Swap PyTorch for TensorFlow or JAX, Hugging Face for native PyTorch, MLflow for Weights & Biases, Triton for SageMaker or Vertex AI, Feast for Tecton. The side panel updates the resume across every mention.

No. Hiring managers screen on substance: the systems you actually shipped, the models that moved a metric, the inference cost or latency wins you can defend in a screen, the evals and safety work you can talk through. Layout origin is not on the rubric. What does cost interviews is a template padded with vague ML-speak, which this one is structured to prevent. The skeleton came from a former Google recruiter; the substance is yours.

Yes, free. Drop your PDF into the review form on this page and a former Google recruiter (me) will read it and email back line-by-line notes inside 12 hours. No upsell, no hidden fee.

Why trust this template

Emmanuel Gendre, former Google recruiter and tech resume writer

Emmanuel Gendre

Former Google recruiter · Tech resume writer

I built this Machine Learning Engineer template from the patterns I saw work, not from generic advice. Below is the data behind every bullet, skills line, and metric placeholder.

  • Experience 900+ ML Engineer resumes screened across LLM, ranking, recsys, and ML-platform stacks during my Google recruiter years and at TechieCV. The Profile Summary and Skills sections mirror what survived the 6-second screen.
  • Expertise Bullets modeled on senior offers. The Anthropic section is structured the way Senior and Staff MLEs write their experience when they land FAANG and AI-lab interviews: ML-system ownership end-to- end, model wins backed by hard metrics, serving cost and latency wins, and structured-eval gating.
  • Trust Stack reflects the 2026 hiring bar. PyTorch + Hugging Face + Triton + MLflow with Feast and Spark is what hiring managers expect today; suggestion chips cover realistic alternatives (TensorFlow, JAX, vLLM, SageMaker, Vertex AI, W&B, Tecton) so you can match your real toolchain without losing keyword fit.
Read my full story →

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Disclaimer. This template is a starting point. Defaults are illustrative; replace every metric and tool with values that reflect your real work. Tailor wording to each job description.