Tailor your CV for Machine Learning Engineers roles
ML engineer postings filter hard on production vocabulary — model serving, MLOps, feature stores, PyTorch — because teams are flooded with CVs from data scientists who trained notebooks but never shipped one. The ATS checks for the stack; the hiring manager scans for latency numbers, deployment frequency, and evidence your models survived contact with real traffic.
Tailor to the role's flavor: inference optimization and GPU serving for platform roles, fine-tuning and RAG pipelines for LLM teams, recommender or ranking systems for product ML. Quantify both model and system: 'cut inference p95 from 400ms to 80ms via ONNX quantization', 'retraining pipeline (Airflow + MLflow) keeps fraud model AUC above 0.92 with weekly refreshes'. Production numbers beat algorithm lists.
Key skills recruiters look for
CV tips that actually move the needle
Pair every model with its production footprint
State serving stack, traffic, and latency: 'Deployed ranking model via Triton on Kubernetes, serving 30M daily requests at p95 90ms'. A model without deployment details reads as a notebook project — the exact stereotype ML engineer CVs must defeat.
Show MLOps lifecycle ownership explicitly
Name experiment tracking (MLflow, Weights & Biases), CI/CD for models, monitoring for drift, and retraining cadence. 'Built drift alerts that caught feature skew, preventing a 12% precision drop' proves you operate models, not just train them.
Quantify business impact alongside model metrics
Translate AUC and recall into outcomes: 'Fraud model blocked $3M in chargebacks annually' or 'Recommendation reranker lifted click-through 14%'. Hiring managers fund ML teams on business results; CVs that speak only in F1 scores stall at the manager screen.
Match the posting's ML domain vocabulary
LLM roles screen for fine-tuning, LoRA, RAG, vector databases, and inference optimization; classic ML roles for feature engineering, XGBoost, and A/B testing. Surface the matching half of your experience first — a mismatched lead section wastes your ten seconds of recruiter attention.
Frequently asked questions
What keywords do ATS scan for in machine learning engineer CVs?
Python, PyTorch, TensorFlow, MLOps, model deployment, feature engineering, and Kubernetes or Docker, plus role-specific terms: LLM, fine-tuning, RAG, and vector databases for generative roles; recommendation systems, ranking, or computer vision for product ML. Include MLflow, SageMaker, or Vertex AI when the posting names a platform.
How long should a machine learning engineer CV be?
One page under eight years; two pages if you have patents or publications directly relevant to the role. Lead with deployed systems and their scale, compress research to a short section, and keep the skills list curated — fifteen frameworks signals tutorials, six with production bullets signals depth.
Data scientist vs. ML engineer CV — what should change?
Shift emphasis from analysis to systems: deployment, serving infrastructure, pipelines, and software engineering practice (testing, code review, Git). Replace dashboard and insight bullets with latency, throughput, and reliability numbers. Add Docker, Kubernetes, and CI/CD evidence — those three gaps reject most data scientists applying to MLE roles.