Machine Learning Engineer Resume Example
Machine Learning Engineer resume example with MLOps achievements, model serving metrics, and the ATS keywords ML hiring teams screen for.
Professional Summary Example
“Machine Learning Engineer with 5+ years building and serving models at scale — from recommendation systems handling 40M daily requests to LLM-powered features in production. Strong in PyTorch, distributed training, and MLOps (MLflow, Kubernetes, feature stores). Cut model serving p99 latency by 64% and training costs by 45% while doubling deployment frequency.”
Experience Bullet Points
Strong bullet points that demonstrate impact with measurable results:
- Built and deployed a two-tower recommendation model serving 40M daily requests at 35ms p99 latency, lifting click-through rate by 18% over the matrix factorization baseline
- Reduced GPU training costs 45% by implementing mixed-precision training, gradient checkpointing, and spot-instance orchestration on Kubernetes
- Shipped an LLM-based support assistant (RAG over 50K documents with fine-tuned reranker) deflecting 31% of tier-1 tickets within 4 months of launch
- Designed a feature store (Feast on Redis) standardizing 400+ features across 6 models and eliminating training-serving skew incidents entirely
- Established CI/CD for ML with automated evaluation gates, shadow deployments, and rollback — increasing model release cadence from monthly to twice weekly
Key Skills
ML Frameworks
MLOps & Serving
LLM & GenAI
Infrastructure
Education
B.S. or M.S. in Computer Science, ML, or related field; production experience and open-source contributions weigh heavier than degrees for senior roles.
Machine Learning Engineer Resume Tips
Quantify serving scale and latency (requests/day, p99 ms) — these numbers instantly separate production ML engineers from research-only candidates.
Show the full lifecycle in your bullets: data, training, deployment, monitoring. MLE roles are hired specifically for the gaps data scientists leave.
Include LLM/GenAI work prominently if you have it — in 2026 it is the single most-searched ML skill cluster by recruiters; RAG and fine-tuning are key phrases.
List cost optimizations (GPU spend, spot instances) — ML infrastructure budgets are scrutinized and savings stories resonate with hiring managers.
Differentiate from data scientist resumes by emphasizing engineering rigor: CI/CD, testing, rollback strategy, training-serving skew.
Common Mistakes to Avoid
Leading with Kaggle competitions instead of production systems
No latency, throughput, or uptime numbers on serving claims
Listing 'LLMs' generically without naming techniques (RAG, LoRA, quantization, evals)
Ignoring data engineering context — MLEs who can't build pipelines get filtered at system design rounds
Treating MLOps tools as buzzwords without an outcome attached to each
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