# Machine Learning Engineer résumé example

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

![Machine Learning Engineer résumé example — mid level, written for United Kingdom, set in the Mono design](https://cvaurum.com/img/examples/machine-learning-engineer.webp)

- Field: Data & Analytics
- Career stage: Mid level
- Written for: United Kingdom
- Design: Mono (https://cvaurum.com/templates/mono)
- Open it: https://cvaurum.com/examples/machine-learning-engineer

Every name, employer, address, phone number and figure below is invented.

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## Samir Qureshi — Machine Learning Engineer

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.

### 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