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Machine Learning Engineer résumé example

Machine learning that actually reached production in healthcare and retail, judged on live metrics rather than offline scores.

Written for United Kingdom, at mid level, in the data & analytics field, shown in the Mono design. Every name, employer, address and number below is invented.

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Samir Qureshi — Machine Learning Engineer

London

Machine learning engineer with seven years putting models into production in healthcare and retail. Owns the referral triage model at a health technology company, where urgent cases now reach a clinician in a median of 40 minutes rather than six hours.

Experience

Machine Learning Engineer, Lumenstack Health · June 2023 – present

Clinical models team of six. Owns triage ranking, the feature store and the release pipeline behind both.

Owns the triage model that orders 9,000 daily clinical referrals; urgent cases now reach a clinician in a median of 40 minutes rather than six hours.
Built the offline and online parity checks that took training-serving skew from 4.1% of predictions to 0.3%.
Cut inference cost 64% by distilling a 340M-parameter classifier into a 22M student with no measurable loss of recall at the operating threshold.
Set up the shadow-deployment pipeline every release now passes through; it caught two models regressing on under-18 referrals before any patient saw them.

Machine Learning Engineer, Bramblewick Retail Group · March 2021 – May 2023

Rebuilt size recommendation across a catalogue of 1.4 million items, cutting fit-related returns from 23% to 16% and saving £2.1m a year in handling.
Replaced a nightly batch scorer with a streaming pipeline, so the first three clicks of a visit changed what a shopper was shown in the same session.
Wrote the drift monitoring that flagged a broken colour taxonomy four days before it would have shown up in sales.

Data Scientist, Wrenfield Insight · September 2019 – February 2021

Built the churn model for a subscription business; its top decile took a retention offer at 3.4 times the control rate.
Automated the weekly modelling refresh, turning a two-day manual routine into 20 minutes and freeing an analyst for experiment design.

Projects

Driftgauge — An open-source feature-drift monitor for scikit-learn and PyTorch serving pipelines.

Education

MEng Computer Science · University of Manchester · September 2015 – June 2019

Skills

Modelling: PyTorch, scikit-learn, XGBoost, Distillation, Calibration
Production: Kubernetes, MLflow, Feature stores, Ray Serve, Shadow deployment
Data: Python, SQL, Spark, Airflow, Snowflake
Practice: Drift monitoring, Experiment design, Clinical safety review, Code review

Why this Machine Learning Engineer résumé works

The summary measures the model by what changed for patients, urgent referrals reaching a clinician in 40 minutes rather than six hours, not by an offline accuracy score.
Production engineering gets equal weight with modelling: training-serving skew cut from 4.1% to 0.3% and a shadow-deployment pipeline that caught two regressions show the work a model needs after it is trained.
Cost is part of the result: distilling a 340M-parameter classifier into a 22M student cut inference cost 64% with no loss of recall, which is the trade-off an ML team lead wants to see judged.
Driftgauge, with 1,900 stars and 24 contributors, gives outside evidence of the same drift-monitoring skill the Bramblewick Retail Group bullet describes.

Machine Learning Engineer résumé: common questions

How long should a machine learning engineer CV be in the UK?

Two pages at most. With seven years, give the current role four bullets and earlier ones two or three, and leave out photo, date of birth and nationality, which UK CVs do not carry.

Should a machine learning engineer resume list model accuracy?

Only beside a live outcome. Offline scores mean little to a hiring manager on their own; pair a metric with what it changed in production, such as returns, response time or cost, and say how the model was deployed and monitored.

Should open-source projects go on an ML engineer resume?

Yes, when they are used by others. Give the project a line with adoption figures (stars, contributors, downloads) and the stack, and link the repository from the header so a reviewer can read the code.