← CV Examples
Machine Learning EngineerMachine Learning Engineer CV example (2026)
An ML engineer CV must prove the 'engineer' half: models in production, serving latency, retraining pipelines, monitoring — not just training metrics. Lead with a summary naming your ML domain (recommendations, LLM applications, forecasting) and deployment stack. Each bullet ideally carries three parts: the model/method, the production machinery around it, and the business number it moved. LLM/RAG work is heavily keyword-scored right now — name the components (embeddings, vector DB, eval harness) explicitly.
What recruiters scan for in a Machine Learning Engineer CV
- 1Production evidence: serving infra, latency, retraining cadence, monitoring/drift
- 2Both keyword families: modelling (PyTorch, XGBoost) and MLOps (MLflow, SageMaker, Airflow)
- 3Business impact of models, not just offline metrics
- 4LLM specifics where relevant: RAG architecture, evals, fine-tuning
Example Machine Learning Engineer CV
A condensed but realistic example — note how every bullet pairs an action with a number, and how tools and methods are named exactly. Details are fictional.
Wei Zhang
Machine Learning Engineer · Recommendations & LLM Systems
Munich, DE · wei.zhang@email.com · github.com/wzhang-ml
Summary
ML engineer with 5 years shipping models to production — recommender systems at marketplace scale, and lately RAG systems for customer support. Comfortable owning the full loop: data pipelines, training, serving at <100ms, and the monitoring that catches drift before users do.
Experience
Machine Learning Engineer
2022 – present
Alpenmarkt (marketplace, 8M users)
- Own the product-recommendation system (two-tower model, PyTorch) serving 40M requests/day at p95 65ms; A/B-tested lift of +6.2% add-to-cart over the previous matrix-factorisation baseline.
- Built a RAG assistant for customer support (pgvector, OpenAI embeddings, custom eval harness with 400 graded cases); deflected 31% of tier-1 tickets with 94% answer-accuracy on the eval set.
- Productionised training with Airflow + MLflow: weekly retraining, shadow deployment, and drift alerts on feature distributions — two silent-degradation incidents caught before impact.
- Cut feature-pipeline compute 40% by moving Spark jobs to incremental processing.
Data Scientist
2020 – 2022
Bergwald Analytics
- Built demand-forecasting models (XGBoost) for retail clients; MAPE improved from 18% to 11% versus client baselines.
- Deployed models as FastAPI services on Kubernetes with CI-driven retraining.
Skills
- Python (PyTorch, scikit-learn, XGBoost)
- LLM systems: RAG, embeddings, evals
- MLflow / Airflow / Spark
- pgvector / Pinecone
- AWS SageMaker · Kubernetes
- Experiment design & A/B testing
Education
MSc Machine Learning, Technical University of Munich, 2020
ATS keywords for Machine Learning Engineer roles
These terms appear most often in Machine Learning Engineer job descriptions and carry the most ATS score weight. Make sure the ones you can honestly claim appear in your CV — in the exact phrasing.
- 1Python ML stack (scikit-learn, XGBoost, PyTorch, TensorFlow)
- 2MLOps and model deployment (MLflow, Kubeflow, SageMaker)
- 3LLM fine-tuning, RAG, and embeddings
- 4Feature engineering and pipeline development (Airflow, Spark)
- 5Vector databases (Pinecone, Weaviate, pgvector)
See how your Machine Learning Engineer CV scores
An example shows the target — the free ATS checker shows your distance from it: score, keyword gaps, and the fixes ranked by impact. And when you're applying to a specific job, the AI Adapt flow rewrites your CV against that exact posting — honestly, without inventing experience. $4 to download.
Machine Learning Engineer CV FAQ
What's the difference between a data scientist CV and an ML engineer CV?
The ML engineer CV is weighted toward production: serving infrastructure, latency, retraining pipelines, monitoring. Offline model metrics alone signal a data scientist. If you're crossing over, add the deployment story to every model bullet you can honestly claim.
How do I present LLM/RAG work credibly?
Name the architecture components (embedding model, vector store, retrieval strategy) and — the differentiator — your evaluation method. 'Built a RAG chatbot' is a weekend project; 'eval harness with 400 graded cases, 94% accuracy' is engineering.
Do research publications belong on an ML engineer CV?
One line with a link if they're relevant and recent — they support depth. But for engineering roles, a deployed system with users outweighs a paper; order the CV accordingly.
More for Machine Learning Engineer