Resume template

ML engineer resume template

This ML engineer template is structured for ATS tools with plain text, standard headings, and quantified bullets. It is role-specific because it balances machine learning terms with production engineering signals: model serving, feature stores, monitoring, Python, PyTorch, Kubernetes, latency, and training pipelines.

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Sample resume

Single-column, plain-text structure with standard headings and quantified bullets.

The person, employers, and figures in this sample are illustrative, written to show the format.

Daniel Okafor

daniel.okafor@example.com+44 20 7946 0158linkedin.com/in/danielokaforgithub.com/danielokafor

Summary

ML engineer with 7 years building production recommendation, fraud, and forecasting systems.

Experienced across Python, PyTorch, feature stores, model serving, CI/CD for ML, monitoring, and distributed training on cloud infrastructure.

Experience

Senior ML Engineer

SignalCart | London, UK | February 2022 - Present

Lead model-serving work for recommendation APIs handling 4,500 requests per second with 80 ms p95 latency.
Built a feature store backed by Redis and BigQuery, reducing training-serving skew incidents from 7 per quarter to 1.
Deployed PyTorch ranking models with shadow evaluation, canary releases, and automated rollback thresholds.
Improved click-through rate by 8.6% through candidate-generation and re-ranking model changes.
Mentor 5 engineers on ML design docs, offline evaluation, and production-readiness reviews.

Machine Learning Engineer

TrustPay | Cambridge, UK | June 2019 - January 2022

Developed fraud detection pipelines using Python, Spark, Kafka, and XGBoost for 12M monthly transactions.
Reduced false positive reviews by 23% while holding chargeback rate flat through threshold calibration.
Automated model retraining and batch scoring in Airflow, cutting manual release work by 10 hours per week.
Implemented drift dashboards for feature freshness, score distributions, and delayed label quality.

Data Engineer

RetailWorks | Nottingham, UK | September 2016 - May 2019

Built ETL pipelines that processed 600GB of sales and inventory data per day.
Implemented data quality checks that reduced failed forecasting jobs by 54%.
Created Python feature pipelines for demand prediction used by merchandising teams.
Optimised Spark jobs to cut nightly training-data build time from 4.5 hours to 95 minutes.

Skills

Languages

Python, SQL, Go, Bash

ML

PyTorch, TensorFlow, scikit-learn, XGBoost, Feature stores

Data

Spark, Kafka, BigQuery, Redis, Airflow

Platform

AWS, Kubernetes, Docker, MLflow, Prometheus

Education

MSc Data Science, University of Edinburgh
BSc Mathematics, University of Leeds

Adapting this template for ML engineer applications

Level determines which half of this hybrid role you emphasise. Earlier in your career, lean on the engineering evidence: the Spark pipelines, the Airflow retraining work, and the data-quality checks, because production reliability is easier to prove than research judgement. At senior level, match the sample's split between serving systems and training infrastructure. For staff roles, foreground the platform work, such as the feature store and the rollback thresholds, since those bullets show you improved how every team works, not just one system.

Recruiters screening for this role check that machine learning words and systems words coexist. A resume that is all PyTorch and no Kubernetes reads as a researcher; all Kafka and no evaluation reads as a data engineer. This template interleaves both on purpose: ranking systems next to p95 latency, retraining next to Airflow, drift monitoring next to dashboards. Reproduce that pairing inside individual bullets rather than separating the two vocabularies into different jobs.

The fastest filters here are claims without operational detail. Saying you took machine learning to production without naming serving latency, request volume, or a rollback mechanism invites doubt. Leaving out evaluation entirely, no precision, recall, or offline-versus-online comparison, suggests you measure success by whether the code ran. And listing every framework from a bootcamp syllabus dilutes the four tools you have actually operated under load.

ATS notes for ML engineer

Balance ML keywords with systems keywords such as Kubernetes, Docker, Spark, Kafka, Redis, and model serving.
Describe model impact with latency, throughput, precision, recall, drift, or business metric movement.
Use Skills categories for ML libraries, data platforms, and cloud tools so keyword matching is literal.
Name feature store, training-serving skew, drift monitoring, and canary release work when present.
Keep GitHub in the contact line because ML engineering recruiters often check implementation evidence.

ML engineer resume questions

How is an ML engineer resume different from a data scientist resume?

The centre of gravity moves from analysis to systems. This template quantifies requests per second, serving latency, training-serving skew, and retraining pipelines, where a data scientist template quantifies experiments and decisions. If your history includes both, pick the framing that matches the job title, because screeners for each role discount the other's vocabulary.

Should I mention drift monitoring and retraining on my resume?

Yes, prominently. Keeping predictions healthy after launch is what separates this role from research work, and hiring managers probe it in interviews anyway. The sample cites drift dashboards, feature freshness, and delayed label quality. Even one bullet showing you noticed degradation and corrected it beats three bullets about training accuracy.

Do certifications help an ML engineering application?

They rarely decide anything on their own. A cloud certification can help a career changer clear an early keyword filter, but interviewers weight operating experience far more: systems you kept fast, pipelines that stopped breaking, costs you cut. If a certification takes a line that a measured production bullet could occupy, choose the bullet.

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ML engineer interview questions

Practise the ml engineer questions recruiters use to test role knowledge, judgement, and depth.

ML engineer salary data

Compare compensation bands before negotiation so the resume, interview story, and target range line up.