Machine Learning Engineer CV Example
For machine learning engineers designing, training, and deploying ML models in production environments. Showcases your expertise in algorithms, data pipelines, and scalable model infrastructure.
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Key Skills to Include
Quick Tips
- Highlight specific ML models you have built, including the problem they solved and measurable outcomes.
- Include your experience with ML frameworks, cloud platforms, and deployment tools like MLflow or Kubeflow.
- Mention any published research, Kaggle competitions, or open-source ML contributions.
- Demonstrate your ability to collaborate with data scientists, engineers, and product teams to deliver ML solutions.
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Upgrade to ProHow to Write Your Machine Learning Engineer CV
A machine learning engineer CV must demonstrate your ability to build and deploy ML models that work reliably in production at scale. Employers want to see that you can bridge the gap between data science experimentation and production engineering — building systems that are not just accurate but also fast, maintainable, and monitorable. Your CV should highlight the models you have built, the infrastructure you have designed, and the business outcomes your ML systems have delivered.
CV Structure
Use a reverse-chronological format with a technical profile, skills section, work experience, and education. Include a dedicated section for publications or open-source contributions if applicable. For each role, describe the ML systems you built, the scale of data and predictions, and the business outcomes achieved. Separate engineering responsibilities from research and modelling work.
CV Format
Choose a modern, clean template that accommodates a technical skills section and links to your GitHub or publications. Keep to two pages and use concise bullet points. Include links to published papers, open-source projects, or technical blog posts. Save as PDF.
CV Profile Examples
Senior Machine Learning Engineer
Machine learning engineer with seven years of experience designing, training, and deploying production ML systems across financial services, telecommunications, and e-commerce sectors. Expert in Python, TensorFlow, and PyTorch with deep experience building recommendation engines, fraud detection models, and demand forecasting pipelines. Delivered ML systems serving 15 million predictions per day with sub-50ms latency on AWS infrastructure.
Machine Learning Engineer — NLP Focus
Machine learning engineer specialising in natural language processing with four years of experience building text classification, named entity recognition, and conversational AI systems. Proficient in fine-tuning transformer models using Hugging Face, building retrieval-augmented generation pipelines, and deploying NLP services using FastAPI and Docker. Published co-author in computational linguistics with one ACL workshop paper.
Mid-Level Machine Learning Engineer
Machine learning engineer with three years of commercial experience building and deploying predictive models for a B2B SaaS platform serving 8,000 enterprise customers. Skilled in Python, scikit-learn, XGBoost, and TensorFlow with hands-on MLOps experience using MLflow and Airflow. Combines strong engineering discipline with statistical rigour to deliver reliable, maintainable ML systems.
State your years of ML engineering experience, primary frameworks, and the types of models you have deployed. Include headline metrics such as prediction volumes, latency figures, or business impact numbers.
Key Skills for Your Machine Learning Engineer CV
Python / TensorFlow / PyTorch
Building ML models and production systems using Python with TensorFlow, PyTorch, and supporting scientific computing libraries.
Model Training & Evaluation
Designing training pipelines, selecting appropriate algorithms, and evaluating model performance using precision, recall, AUC, and business metrics.
Feature Engineering
Creating, transforming, and selecting features from raw data to improve model performance and predictive accuracy.
Data Pipeline Development
Building scalable data pipelines using Airflow, Spark, or custom ETL systems for model training and inference data.
MLOps & Model Deployment
Deploying models to production using SageMaker, MLflow, or Kubernetes with monitoring, versioning, and automated retraining.
Deep Learning
Designing and training deep neural networks including CNNs, RNNs, transformers, and attention-based architectures.
Natural Language Processing
Building NLP systems including text classification, named entity recognition, and language generation using transformer models.
Cloud ML Platforms
Using AWS SageMaker, Google Vertex AI, or Azure ML for scalable model training, deployment, and management.
Statistical Analysis
Applying statistical methods including hypothesis testing, regression analysis, and Bayesian inference to inform modelling decisions.
Work Experience Examples
For each role, describe the ML systems you built, the data scale, and the production infrastructure. Detail your end-to-end involvement from data processing through model training to deployment and monitoring. Include quantified outcomes — accuracy improvements, latency reductions, business metrics impact, or cost savings from ML-driven decisions.
Senior Machine Learning Engineer
Spectrum Retail Technologies
Designed and deployed machine learning systems powering personalisation, demand forecasting, and pricing optimisation for an e-commerce platform with 8 million registered customers.
Responsibilities
- Built a collaborative filtering recommendation engine using matrix factorisation and deep learning embeddings, serving 12 million recommendations per day.
- Designed end-to-end ML pipelines using Apache Airflow for data ingestion, feature engineering, model training, and evaluation across 200 product categories.
- Deployed models to production using AWS SageMaker endpoints with A/B testing infrastructure for controlled rollouts and performance monitoring.
- Optimised model inference latency from 120ms to 35ms through model quantisation, batch prediction, and efficient feature store design.
- Collaborated with product managers and data analysts to define success metrics, evaluate model impact, and prioritise ML roadmap items.
Achievements
- Increased personalised product recommendation click-through rates by 32%, contributing an estimated £2.8M in additional annual revenue.
- Reduced inventory waste by 18% through a demand forecasting model that improved purchasing accuracy across seasonal product lines.
- Built a reusable ML pipeline framework adopted by the entire data science team, reducing new model deployment time from two weeks to two days.
Machine Learning Engineer
Sentinel Fraud Prevention
Developed and maintained real-time fraud detection models for a fintech company processing 4 million transactions per day across payment and lending products.
Responsibilities
- Trained gradient boosting and neural network models for real-time transaction fraud scoring, achieving 94% precision at 85% recall.
- Engineered features from transaction data, device fingerprints, and behavioural signals using a real-time feature store built on Redis.
- Implemented model monitoring dashboards tracking data drift, prediction distributions, and model performance degradation over time.
- Worked with the compliance team to ensure model decisions met regulatory explainability requirements under FCA guidelines.
Achievements
- Reduced fraud losses by £1.4M annually through an improved detection model that identified 22% more fraudulent transactions than the previous system.
- Decreased false positive rates by 40% through feature engineering improvements, reducing legitimate customer friction during the payment process.
Education & Qualifications
Lead with your MSc or PhD if in machine learning, data science, or a closely related field. Include relevant certifications from AWS, Google, or TensorFlow. Mention published papers, conference presentations, or teaching experience.
MSc / PhD Machine Learning or Data Science
An advanced degree demonstrating deep theoretical and practical expertise in machine learning algorithms and systems.
AWS Machine Learning Specialty
A certification validating skills in building, training, tuning, and deploying ML models on AWS.
TensorFlow Developer Certificate
A Google-issued certification demonstrating proficiency in building production ML models using TensorFlow.
Google Professional Machine Learning Engineer
A certification validating the ability to design and deploy ML solutions on Google Cloud Platform.
Frequently Asked Questions
What differentiates a machine learning engineer from a data scientist on a CV?
How important is software engineering experience for ML engineer roles?
Should I include Kaggle or competition experience on my ML engineer CV?
How do I describe ML model performance on my CV?
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