Resume Example

Machine Learning Engineer Resume Examples

Use these ATS-friendly resume examples and templates to showcase your models shipped, metrics moved, and systems built as a Machine Learning Engineer.
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A strong machine learning engineer resume should show models in production and the business or system metric each one moved. Highlight predictions served, online metric lifts from A/B tests, model accuracy or error improvements, latency and cost reductions, deployment speed, and the scale of data and users. Name your frameworks and platforms, because PyTorch, cloud machine learning services, and orchestration tools are filtered on. State your specialism plainly, since an engineer shipping recommendation models, a senior engineer leading a fraud team, an entry level engineer with a first production model, a deep learning engineer in medical imaging, an ML ops engineer running a platform, and an ML ops data engineer owning a feature store are hired on different evidence. Use the examples below to see how to turn machine learning work into clear, results-focused resume achievements.

Machine Learning Engineer Resume Example

Meet Tenzin Dorjee, a fictional machine learning engineer owning 4 recommendation and ranking models serving about 900M predictions a day. This example shows the core role measured on online metric lifts, training cost, and monitoring that catches regressions.

Tenzin Dorjee

(206) 555-0149 ◇ Seattle, WA

tenzin.dorjee@example.com ◇ linkedin.com/in/tenzin-dorjee ◇ tenzindorjee.com

Objective

Machine learning engineer with 6 years building and shipping recommendation and ranking models for an e-commerce marketplace with 40M monthly users. Owns 4 production models serving about 900M predictions a day, lifted add-to-cart rate 11% with a two-tower retrieval model, and cut ranking model training cost 45% by moving to a distributed feature pipeline. Measures every model by the online metric it moved, and ships with the monitoring to prove it.

Education
M.S. in Computer Science, Machine Learning Track, Puget Sound Crown University 2018 – 2020

Coursework in Statistical Learning, Deep Learning, Large-Scale Systems, and Information Retrieval.

B.S. in Computer Science, Puget Sound Crown University 2014 – 2018

AWS Certified Machine Learning Specialty earned in 2022.

Skills
Machine Learning
Recommendation and ranking systems, two-tower retrieval, gradient boosted models, embeddings, offline evaluation, A/B test design and analysis, feature engineering
Engineering
Python, SQL, Scala, distributed training, low-latency model serving, feature pipelines, batch and streaming inference, model monitoring
Practices
Experiment tracking, model versioning, canary rollouts, on-call for models, code review, design documents, cross-functional work with product and data science
Tools & Platforms
PyTorch, TensorFlow, Spark, Kubernetes, AWS SageMaker, Feast, MLflow, Airflow
Experience
Machine Learning Engineer II 03/2023 – Present
Puget Sound Crown Marketplace Seattle, WA
  • Own 4 production recommendation and ranking models serving about 900M predictions a day for 40M monthly users at a p99 latency under 40 milliseconds.
  • Lifted add-to-cart rate 11% and revenue per session 6% with a two-tower retrieval model validated across a 4-week A/B test on 8M users.
  • Cut ranking model training cost 45%, about $380,000 a year, by moving feature generation to a distributed Spark pipeline with point-in-time joins.
Machine Learning Engineer 07/2020 – 02/2023
Cascadia Line Commerce Seattle, WA
  • Built a gradient boosted search ranking model that raised click-through rate 8% across 12M searches a day.
  • Shipped 9 models to production in 3 years with a canary rollout process that caught 3 regressions before full traffic.
  • Built model monitoring for feature drift and prediction distribution across 6 models, cutting undetected degradation incidents from 5 a year to 0.
Machine Learning Intern 06/2019 – 09/2019
Cascadia Line Commerce Seattle, WA
  • Built a product categorisation model across 2,400 categories that reached 94% accuracy, replacing a rules system at 81%.
  • Reduced categorisation inference cost 60% through model distillation to a smaller architecture.
  • Received a return offer at the end of the 14-week internship based on the model shipping to production.
Projects

Two-Tower Retrieval Model. Designed and shipped a two-tower retrieval model over 30M items and 40M users with in-batch negatives and an approximate nearest neighbour index refreshed hourly, which lifted add-to-cart 11% in a 4-week A/B test and replaced a collaborative filtering system that could not handle new items.

Distributed Feature Pipeline. Rebuilt feature generation for the ranking model as a Spark pipeline with point-in-time correct joins over 18 months of event data, which removed training-serving skew found in 3 features, cut training cost 45%, and cut the retraining cycle from 5 days to 1.

Model Monitoring System. Built a monitoring system tracking feature drift, prediction distribution, and online metric deltas for 6 models with alerts to the on-call engineer, which caught a silent upstream schema change within 2 hours that would have degraded ranking for a week.

Extra-Curricular Activities
  • Co-organise a city recommender systems meetup of about 300 members that meets every 2 months.
  • Reviewer for an industry recommender systems workshop, about 8 papers a year.
  • Volunteer mentor for a machine learning bootcamp, coaching 4 students a cohort.
Leadership
  • Technical lead for a recommendations team of 5 engineers and 2 data scientists.
  • Mentored 3 junior engineers, 2 of whom shipped their first production model within 6 months.
  • Run the weekly model review where every experiment is evaluated before an A/B test is launched.
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Senior Machine Learning Engineer Resume Example

Meet Valentina Castellanos, a senior engineer leading a team of 8 that owns real-time fraud detection across $60B in payment volume. This example shows senior scope: fraud losses and false declines cut together, a feature platform, and model governance that passed regulatory review.

Valentina Castellanos

(415) 555-0182 ◇ San Francisco, CA

valentina.castellanos@example.com ◇ linkedin.com/in/valentina-castellanos ◇ valentinacastellanos.com

Objective

Senior machine learning engineer with 11 years building fraud and risk models for payments companies, currently leading a team of 8 that owns real-time fraud detection across $60B in annual payment volume. Cut fraud losses 38% while reducing false declines 22%, built the real-time feature platform serving 12 models at 15 milliseconds p99, and designed the model governance process that passed 2 regulatory model risk reviews. Builds models that make a decision in milliseconds and can be explained afterwards.

Education
M.S. in Statistics, Golden Gate Crown University 2013 – 2015

Coursework in Statistical Learning, Bayesian Methods, Time Series, and Computational Statistics.

B.S. in Mathematics and Computer Science, Golden Gate Crown University 2009 – 2013

Google Cloud Professional Machine Learning Engineer certification earned in 2023.

Skills
Machine Learning
Fraud and risk modelling, gradient boosting and neural networks on tabular data, graph features, imbalanced classification, online learning, model explainability, adversarial drift
Systems
Real-time feature platforms, streaming inference, low-latency serving, model governance and audit trails, shadow deployment, champion-challenger frameworks
Leadership
Team leadership and hiring, technical roadmap, architecture review, cross-functional work with risk, compliance, and product, executive and regulator communication
Tools & Platforms
Python, Scala, XGBoost, PyTorch, Kafka, Flink, Kubernetes, Google Cloud Vertex AI
Experience
Senior Machine Learning Engineer, Fraud and Risk 01/2021 – Present
Golden Gate Crown Payments San Francisco, CA
  • Lead a team of 8 engineers owning real-time fraud detection across $60B in annual payment volume and about 2B transactions a year.
  • Cut fraud losses 38%, about $41M a year, while reducing false declines 22% through a graph-feature model and a threshold optimisation framework.
  • Built a real-time feature platform serving 12 models at 15 milliseconds p99 that replaced 4 team-specific pipelines.
Machine Learning Engineer 06/2017 – 12/2020
Embarcadero Line Financial San Francisco, CA
  • Built the account takeover detection model that cut takeover losses 54% across 30M accounts in its first year.
  • Designed the champion-challenger framework that let 6 models be evaluated in shadow mode before promotion, cutting bad deployments to 0 in 3 years.
  • Reduced model retraining cycle from monthly to weekly through an automated pipeline, which improved fraud capture 9% against adversarial drift.
Data Scientist, Risk 08/2015 – 05/2017
Embarcadero Line Financial San Francisco, CA
  • Built a credit risk scorecard used for 400,000 applications a year that cut default rate 15% at the same approval rate.
  • Delivered 14 analyses to the risk committee that changed 6 policy thresholds.
  • Wrote the feature library of 300 risk features that became the base for the first machine learning fraud model.
Projects

Graph-Feature Fraud Model. Built a fraud model with graph features over shared devices, cards, and addresses computed in real time across 2B transactions a year, which cut fraud losses 38% and, combined with a cost-weighted threshold framework, cut false declines 22% worth about $90M in recovered good volume.

Real-Time Feature Platform. Designed a streaming feature platform on Kafka and Flink with a low-latency store, point-in-time backfills for training, and a feature registry, which serves 12 models at 15 milliseconds p99, removed training-serving skew, and cut new model time to production from 4 months to 6 weeks.

Model Governance Process. Designed the model governance process covering documentation, validation, explainability reports, monitoring, and approval gates for 20 production models, which passed 2 regulatory model risk reviews with 0 findings and is now the company standard.

Extra-Curricular Activities
  • Speak at machine learning and payments conferences about 3 times a year on real-time fraud systems.
  • Serve on the programme committee of an applied machine learning conference, reviewing about 15 papers a year.
  • Mentor 3 women in machine learning a year through an industry mentorship programme.
Leadership
  • Lead a team of 8 engineers with hiring, roadmap, and architecture ownership, and 3 promoted in 4 years.
  • Present fraud model performance quarterly to the risk committee and the executive team.
  • Chair the machine learning architecture review board across 5 teams and 40 engineers.
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Entry Level Machine Learning Engineer Resume Example

Meet Kwesi Mensah, 10 months into a first machine learning role after a master degree and 2 internships. This example shows how to write an early-career resume on a model that actually ran in production, a first solo improvement, and a leakage bug found and fixed.

Kwesi Mensah

(412) 555-0116 ◇ Pittsburgh, PA

kwesi.mensah@example.com ◇ linkedin.com/in/kwesi-mensah ◇ kwesimensah.com

Objective

Entry level machine learning engineer with 10 months at a logistics technology company after a master degree, 2 internships, and a capstone project that shipped. Owns the retraining pipeline for a delivery time prediction model serving 1.2M predictions a day, cut its mean absolute error 14% in a first solo project, and fixed a data leakage bug that had inflated offline metrics for 6 months. Early in the career, so every number here is from a model that actually ran in production.

Education
M.S. in Machine Learning, Allegheny Crown University 2023 – 2025

GPA 3.8; coursework in Deep Learning, Probabilistic Graphical Models, Machine Learning Systems, and Optimisation.

B.S. in Computer Science, Allegheny Crown University 2019 – 2023

AWS Certified Machine Learning Specialty earned in 2025.

Skills
Machine Learning
Supervised learning, gradient boosting, neural networks, feature engineering, cross-validation and leakage prevention, offline evaluation, error analysis
Engineering
Python, SQL, training and inference pipelines, unit and data tests, containerisation, experiment tracking, model serving basics, Git workflow
Practices
Code review, design documents, reproducible experiments, on-call shadowing, working with data engineers and product managers, clear write-ups
Tools & Platforms
PyTorch, scikit-learn, XGBoost, Airflow, Docker, AWS SageMaker, MLflow, dbt
Experience
Machine Learning Engineer I 11/2025 – Present
Allegheny Crown Logistics Technology Pittsburgh, PA
  • Own the weekly retraining pipeline for a delivery time prediction model serving about 1.2M predictions a day across 14 metro areas.
  • Cut mean absolute error 14% in a first solo project by adding 20 route and weather features and tuning the loss for late deliveries.
  • Found and fixed a data leakage bug in the evaluation split that had inflated offline metrics 9% for 6 months, and rebuilt the evaluation set.
Machine Learning Intern 05/2025 – 08/2025
Allegheny Crown Logistics Technology Pittsburgh, PA
  • Built a package damage classifier from 40,000 depot images that reached 92% precision at 85% recall and shipped to 3 depots.
  • Wrote data validation tests for 6 pipelines that caught 2 upstream schema changes during the internship.
  • Received a return offer at the end of the 14-week internship based on the classifier deployment.
Data Science Intern 05/2024 – 08/2024
Monongahela Line Analytics Pittsburgh, PA
  • Built a customer churn model for a subscription client across 300,000 accounts that improved lift in the top decile by 2.1 times over the existing model.
  • Cleaned and documented a 90-table data warehouse extract used by 4 analysts.
  • Presented findings to the client in 3 review meetings, with the model adopted for the retention campaign.
Projects

Delivery Time Model Improvement. Ran an error analysis across 2M predictions to find that late deliveries in 3 metro areas drove most of the error, added 20 route density and weather features, and reweighted the loss toward late cases, which cut mean absolute error 14% and late-delivery error 23% in a 3-week shadow test.

Evaluation Leakage Fix. Traced a gap between offline and online metrics to a random split that placed the same route on both sides, rebuilt the evaluation as a time-based split with route grouping, and documented the standard, which is now applied to all 5 models on the team.

Capstone: Warehouse Demand Forecasting. Led a 4-person capstone team building a demand forecasting model for a regional grocery chain across 120 stores and 2,000 products, which cut forecast error 18% against the existing system and was adopted for 1 distribution centre after the project.

Extra-Curricular Activities
  • Member of a city machine learning meetup of about 500, attending monthly sessions.
  • Placed in the top 5% of 3,000 teams in a public tabular machine learning competition in 2024.
  • Volunteer coding instructor for a high school programme, 2 hours a week across the school year.
Leadership
  • Led a 4-person capstone team through scoping, modelling, and client delivery.
  • Onboarded 2 interns in the first year on the team, both shipping a pipeline change by week 4.
  • Run a fortnightly paper reading session for a team of 7 engineers.
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Deep Learning Engineer Resume Example

Meet Anushka Chatterjee, a deep learning engineer owning segmentation and detection models in a regulatory-cleared radiology product. This example shows the deep learning specialism: sensitivity gains, inference optimisation, and a validation study.

Anushka Chatterjee

(617) 555-0173 ◇ Boston, MA

anushka.chatterjee@example.com ◇ linkedin.com/in/anushka-chatterjee ◇ anushkachatterjee.com

Objective

Deep learning engineer with 7 years building computer vision models for medical imaging, currently owning the segmentation and detection models in a regulatory-cleared radiology software product used at 140 hospitals. Raised lung nodule detection sensitivity from 87% to 94% at the same false positive rate, cut inference time 70% through model optimisation for hospital hardware, and led the validation study for a regulatory clearance. Trains models on data that is scarce, noisy, and consequential, and validates them as if a patient depends on the result.

Education
Ph.D. in Electrical Engineering, Computer Vision, Charles River Crown University 2014 – 2019

Dissertation on weakly supervised segmentation for medical images; 6 peer-reviewed publications.

B.Tech in Electronics and Communication Engineering, Charles River Crown University 2010 – 2014

Completed a medical device software lifecycle course under IEC 62304 in 2021.

Skills
Deep Learning
Convolutional and transformer architectures, 3D segmentation and detection, self-supervised pretraining, weak and semi-supervised learning, uncertainty estimation, data augmentation for scarce data
Model Optimisation
Quantisation, pruning, distillation, ONNX and TensorRT deployment, GPU and CPU inference profiling, mixed precision training, distributed training
Medical Imaging and Validation
DICOM and CT and MRI data handling, annotation protocol design, reader studies, regulatory validation, bias and subgroup analysis, documentation for submissions
Tools & Platforms
PyTorch, MONAI, TensorRT, ONNX Runtime, Weights and Biases, NVIDIA Clara, Kubernetes, AWS
Experience
Senior Deep Learning Engineer 02/2022 – Present
Charles River Crown Medical AI Boston, MA
  • Own the segmentation and detection models in a radiology product used at 140 hospitals processing about 9,000 CT studies a day.
  • Raised lung nodule detection sensitivity from 87% to 94% at 1 false positive per scan through a 3D architecture and self-supervised pretraining on 400,000 unlabelled scans.
  • Cut inference time 70%, from 48 seconds to 14 per study, through quantisation and TensorRT deployment on hospital GPU hardware.
Deep Learning Engineer 09/2019 – 01/2022
Back Bay Line Imaging Boston, MA
  • Built the brain hemorrhage detection model that reached 96% sensitivity across a validation set of 12,000 studies from 8 sites.
  • Led the validation study for a regulatory clearance, designing a 3-reader study over 1,500 cases that was cleared without additional data requests.
  • Designed the annotation protocol and quality control process for 60,000 labelled studies across 15 annotators with a 0.91 inter-rater agreement.
Doctoral Researcher 09/2014 – 08/2019
Charles River Crown University, Vision Laboratory Boston, MA
  • Published 6 peer-reviewed papers on weakly supervised medical image segmentation with about 900 citations.
  • Built a segmentation method that matched fully supervised performance using 10% of the labels on 3 public benchmarks.
  • Mentored 4 master students through thesis projects, 2 of whom published.
Projects

Lung Nodule Detection Upgrade. Replaced a 2D detection model with a 3D architecture pretrained on 400,000 unlabelled scans using self-supervision, then fine-tuned on 18,000 labelled studies, which raised sensitivity from 87% to 94% at 1 false positive per scan and held across 5 scanner manufacturers in subgroup analysis.

Inference Optimisation. Profiled the inference pipeline, applied 8-bit quantisation with a 0.2 point sensitivity cost, converted to TensorRT, and batched preprocessing, which cut study processing from 48 seconds to 14 on the GPU hardware hospitals already had and removed the need for a hardware upgrade at 140 sites.

Regulatory Validation Study. Designed and ran a 3-reader, 1,500-case validation study across 8 sites with prespecified endpoints, subgroup analysis by age, sex, and scanner, and a full statistical analysis plan, which supported a regulatory clearance with 0 additional data requests.

Extra-Curricular Activities
  • Reviewer for 2 medical imaging conferences, reviewing about 20 papers a year.
  • Speak at medical imaging and machine learning conferences about twice a year.
  • Volunteer mentor for a women in computing programme, coaching 3 students a year.
Leadership
  • Technical lead for a modelling team of 6 engineers and 2 clinical annotators.
  • Mentored 5 engineers across 4 years, 3 of whom now own a production model.
  • Represent engineering in the clinical advisory board of 8 radiologists that meets quarterly.
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ML Ops Engineer Resume Example

Meet Dario Benedetti, an ML ops engineer owning a platform that runs 85 production models for 30 data scientists. This example shows platform evidence: deployment time, serving availability, and GPU spend.

Dario Benedetti

(720) 555-0159 ◇ Denver, CO

dario.benedetti@example.com ◇ linkedin.com/in/dario-benedetti ◇ dariobenedetti.com

Objective

ML ops engineer with 7 years in platform and infrastructure, currently owning the machine learning platform that runs 85 production models for a healthcare analytics company with 30 data scientists. Cut model deployment time from 3 weeks to 2 days, raised model serving availability from 99.2% to 99.95%, and cut GPU training spend 41% through scheduling and spot capacity. Builds the platform so that data scientists ship models without filing a ticket, and so that the models keep running when nobody is watching.

Education
B.S. in Computer Engineering, Front Range Crown University 2015 – 2019

Coursework in Distributed Systems, Operating Systems, Cloud Computing, and Machine Learning.

Certified Kubernetes Administrator, Cloud Native Computing Foundation 2021

Also holds AWS Solutions Architect Professional and Google Cloud Professional Machine Learning Engineer certifications.

Skills
ML Platform
Model registry and versioning, CI and CD for models, automated training pipelines, model serving and autoscaling, canary and shadow deployment, rollback
Infrastructure
Kubernetes, infrastructure as code, GPU cluster scheduling, cost optimisation, observability and alerting, secrets and access control, multi-tenant platforms
Reliability and Governance
Service level objectives for models, drift and performance monitoring, incident response, audit trails and lineage, compliance in regulated environments, on-call
Tools & Platforms
Kubeflow, MLflow, Terraform, Argo Workflows, Seldon, Prometheus and Grafana, AWS, Python and Go
Experience
Senior ML Ops Engineer 05/2022 – Present
Front Range Crown Health Analytics Denver, CO
  • Own the machine learning platform running 85 production models and serving about 40M predictions a day for 30 data scientists across 6 teams.
  • Cut model deployment time from 3 weeks to 2 days with a self-service pipeline from registry to canary to full traffic, used for 210 deployments last year.
  • Raised model serving availability from 99.2% to 99.95% through autoscaling, health checks, and automated rollback on metric regression.
ML Ops Engineer 08/2020 – 04/2022
South Platte Line Insurance Technology Denver, CO
  • Built the first model registry and CI pipeline for a team of 12 data scientists, moving 30 models from notebooks into versioned, tested deployments.
  • Cut GPU training spend 41%, about $520,000 a year, by scheduling jobs on spot capacity with checkpointing and right-sizing 40 training workloads.
  • Built drift monitoring for 30 models that caught 7 silent data quality incidents in the first year.
Site Reliability Engineer 07/2019 – 07/2020
South Platte Line Insurance Technology Denver, CO
  • Ran on-call for a platform of 60 services handling about 8,000 requests a second with a 99.9% availability target met in 11 of 12 months.
  • Migrated 25 services to Kubernetes with infrastructure as code, cutting environment provisioning from 2 days to 20 minutes.
  • Cut mean time to recovery from 55 minutes to 18 through runbooks and alert tuning that removed 70% of noisy pages.
Projects

Self-Service Deployment Pipeline. Built a pipeline that takes a registered model through automated tests, containerisation, canary at 5% traffic with metric comparison, and promotion or rollback, with an audit trail for every step, which cut deployment from 3 weeks to 2 days and let data scientists deploy 210 times last year without a platform ticket.

GPU Cost Reduction. Moved training jobs to spot capacity with automatic checkpointing and resume, built a queue that packs jobs by GPU memory, and right-sized 40 workloads from profiling data, which cut GPU spend 41% while cutting median queue time from 4 hours to 35 minutes.

Model Service Level Objectives. Defined availability, latency, and prediction quality objectives for 85 models with dashboards and paging thresholds, and an automated rollback when a canary regresses a quality metric, which raised availability to 99.95% and cut model incidents from 14 a year to 3.

Extra-Curricular Activities
  • Co-organise a city ML ops meetup of about 400 members that meets every 6 weeks.
  • Contributor to 2 open source machine learning platform projects, with about 30 merged pull requests.
  • Speak at cloud and machine learning conferences about twice a year on model deployment.
Leadership
  • Technical lead for a platform team of 5 engineers supporting 30 data scientists.
  • Trained 30 data scientists on the deployment pipeline through 8 workshops and a written guide.
  • Own the machine learning platform roadmap and present it quarterly to engineering leadership.
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ML Ops Data Engineer Resume Example

Meet Freya Sandqvist, an ML ops data engineer owning the feature store and training pipelines behind 40 models at a fintech company. This example shows the data side of ML ops: feature serving scale, point-in-time training data, and training-serving skew eliminated.

Freya Sandqvist

(801) 555-0131 ◇ Salt Lake City, UT

freya.sandqvist@example.com ◇ linkedin.com/in/freya-sandqvist ◇ freyasandqvist.com

Objective

ML ops data engineer with 6 years building the data side of machine learning platforms, currently owning the feature store and training data pipelines that feed 40 production models at a fintech company. Built a feature store with 1,800 features serving 20,000 lookups a second, cut training data preparation from 4 days to 3 hours with point-in-time correct pipelines, and eliminated training-serving skew incidents from 9 a year to 0. Owns the data that models are trained and served on, and makes sure those 2 things are the same.

Education
B.S. in Computer Science, Wasatch Crown University 2016 – 2020

Coursework in Databases, Distributed Systems, Data Mining, and Machine Learning.

Databricks Certified Data Engineer Professional, Databricks 2023

Also holds Google Cloud Professional Data Engineer and AWS Certified Machine Learning Specialty certifications.

Skills
ML Data Engineering
Feature stores, point-in-time correct training data, streaming and batch feature pipelines, training-serving consistency, data versioning, dataset lineage
Data Platform
Spark and Flink, lakehouse architecture, orchestration, data quality testing, schema management, low-latency key-value serving, cost optimisation
ML Ops Practice
Pipeline CI and CD, monitoring for data drift and freshness, reproducible training datasets, working with data scientists on feature definitions, on-call for data pipelines
Tools & Platforms
Python, SQL, Scala, Spark, Kafka, Databricks, Feast, Airflow, Redis, Great Expectations
Experience
ML Ops Data Engineer 01/2023 – Present
Wasatch Crown Financial Technology Salt Lake City, UT
  • Own the feature store and training data pipelines feeding 40 production models for 18 data scientists at a fintech company.
  • Built a feature store with 1,800 features across streaming and batch sources serving 20,000 lookups a second at 8 milliseconds p99.
  • Eliminated training-serving skew incidents from 9 a year to 0 across 3 years by serving training and inference features from 1 definition.
Data Engineer, Machine Learning Platform 07/2020 – 12/2022
Great Basin Line Software Salt Lake City, UT
  • Cut training data preparation from 4 days to 3 hours by building point-in-time correct pipelines over 2 years of event data for 12 models.
  • Built data quality checks across 60 pipelines that caught 23 upstream breaks in 2 years before they reached a model.
  • Cut pipeline compute cost 35%, about $200,000 a year, through incremental processing and partition pruning.
Data Engineering Intern 05/2019 – 08/2019
Great Basin Line Software Salt Lake City, UT
  • Migrated 15 batch jobs from a legacy scheduler to Airflow with tests, cutting failed runs 60%.
  • Built a dataset documentation generator covering 200 tables that was adopted by the analytics team.
  • Received a return offer at the end of the 14-week internship based on the migration work.
Projects

Feature Store Build. Designed a feature store with a registry of 1,800 feature definitions, batch materialisation from the lakehouse, streaming features from Kafka through Flink, and a low-latency online store, which serves 20,000 lookups a second and cut new feature time to production from 3 weeks to 2 days.

Point-in-Time Training Pipelines. Built training dataset generation with point-in-time joins across 2 years of events and 400 features, versioned every dataset, and validated against served features on a sample, which cut preparation from 4 days to 3 hours and removed 3 label leakage bugs found in existing models.

Data Quality Gate. Added schema, freshness, distribution, and null-rate checks to 60 pipelines with a gate that blocks a retraining run when a check fails, which caught 23 upstream breaks in 2 years and cut model retraining on bad data to 0.

Extra-Curricular Activities
  • Member of a city data engineering meetup of about 350, presenting about once a year.
  • Contributor to an open source feature store project with about 15 merged pull requests.
  • Volunteer data mentor for a nonprofit analytics programme, coaching 2 fellows a year.
Leadership
  • Lead the feature platform workstream of 3 engineers within a machine learning platform team of 9.
  • Trained 18 data scientists on feature definition standards and the training dataset workflow.
  • Chair the feature review that approves about 40 new feature definitions a month.
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