Senior AI/ML Engineer for Ads Ranking
Resume:
Michael Stagg
Senior AI/ML Engineer Ads Ranking & Marketing ML
Venice, FL
***************@*******.***
PROFESSIONAL SUMMARY
Senior AI/ML Engineer with 10+ years of experience delivering production-grade ML systems for marketing, advertising, and personalization use cases. Specialized in end-to-end ML pipelines, real-time decision-making, and experimentation at scale, with a strong foundation in data engineering and cloud platforms. Experienced in driving measurable business impact by turning complex customer and advertiser signals into scalable, high-performance AI solutions.
1B+
Ad impressions/day
500+
Experiments/year
$200M+
Incremental revenue
100K+
Advertisers impacted
TECHNICAL SKILLS
Core Engineering & Data
Python, SQL, Scala/Spark, Java, Bash/Shell, data modeling, schema design, data quality, ETL/ELT, batch and streaming processing
Machine Learning & Decisions
Supervised and unsupervised learning, feature engineering, model evaluation, class imbalance, time-series/demand modeling, SHAP, LIME AI, NLP & Generative Systems
PyTorch, TensorFlow, Keras, Hugging Face, BERT, RoBERTa, NER, text classification, embeddings, prompt engineering, RAG MLOps & Experimentation
Production ML pipelines, feature stores, deployment, monitoring, drift detection, A/B testing, CI/CD, experiment tracking, governance
Systems & APIs
Microservices, event-driven architecture, REST APIs, FastAPI, Flask, low-latency inference services
Cloud & Analytics
AWS S3, EMR, Glue, Lambda, SageMaker, Redshift, DynamoDB; GCP BigQuery, Dataflow, Vertex AI; Spark, Kafka, Airflow, Snowflake, Tableau, Power BI PROFESSIONAL EXPERIENCE
Amazon Senior AI/ML Engineer Seattle, WA Aug 2022 - Present
• Spearheaded ads ranking and bidding models powering Sponsored Products experiences and serving 1B+ ad impressions per day across Amazon marketplaces.
• Created LTV, conversion probability, and budget-pacing models used by 100K+ advertisers, improving ROAS by 10-18% across key verticals.
• Developed real-time inference services handling 100K+ requests/second with <30ms p95 latency, supporting auction-time decisions.
• Led large-scale online experimentation across A/B and multivariate tests, running 500+ experiments per year and driving $200M+ incremental annual revenue.
• Implemented model monitoring and drift detection across 200+ production models, reducing performance regressions by 40%. Adobe Machine Learning Engineer San Jose, CA May 2018 – Jul 2022
• Led development of real-time personalization and recommendation models serving 3,000+ enterprise customers across web, email, and mobile marketing channels.
• Architected low-latency inference services handling 50K+ requests/second with <100ms p90 latency, enabling real-time customer journey optimization.
• Maintained feature stores powering 100+ ML models and user-level signals from 1B+ monthly consumer profiles.
• Implemented A/B testing and experimentation frameworks used in 1,200+ customer experiments, driving 8-15% uplift in conversion and engagement metrics.
• Partnered with product and UX teams to integrate AI-driven decisions into Adobe Experience Platform workflows, influencing $1B+ in annual marketing spend.
• Mentored 5-7 engineers and data scientists, setting best practices for model lifecycle management, monitoring, and retraining at scale. Experian Machine Learning Engineer Costa Mesa, CA Nov 2016 – Apr 2018
• Deployed identity resolution models linking 200M+ consumer profiles across online and offline data sources, improving cross-channel targeting accuracy by 35%.
• Programmed lookalike and propensity models scoring 50M+ users daily for acquisition, churn, and conversion use cases.
• Designed end-to-end ML pipelines processing 10+ TB/day of marketing and behavioral data from feature engineering through batch inference.
• Created privacy-aware data handling with PII masking and consent-based features, reducing compliance incidents by 40% while maintaining model performance.
• Improved model retraining and inference efficiency, cutting batch scoring runtime by 45% through feature reduction and pipeline optimization. Nielsen Sr. Data Analyst New York, NY Jun 2013 – Oct 2016
• Designed and maintained ETL pipelines processing 5-8B+ media interaction records per month, supporting national TV and digital audience measurement products.
• Built audience segmentation and aggregation models covering 50M+ households, enabling reach and frequency analysis across 1,000+ concurrent marketing campaigns.
• Optimized batch processing workflows in SQL/Hive, reducing daily processing time by ~30% and improving report availability for downstream analytics teams.
• Implemented data quality validation rules across 20+ upstream sources, reducing reporting discrepancies by 25% and improving client trust in published metrics.
EDUCATION
University of Florida, Gainesville, FL 2009 - 2013 Bachelor's Degree in Computer Science
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