Senior/Machine Learning Engineer — Performance Optimization

What you'll do

• Build, train, evaluate, and improve machine learning models for prediction, ranking, campaign optimization, bidding, forecasting, and calibration
• Work with large-scale datasets from auctions, impressions, clicks, video events, conversions, users, context, inventory, campaigns, and marketplace feedback
• Develop and improve features, training datasets, labels, and evaluation workflows for performance advertising models
• Analyze model performance across offline metrics, online experiments, campaign outcomes, and business KPIs
• Help improve models for CTR, CVR, VCR, CPA, ROAS, app-install, user-value, and campaign-performance optimization
• Work with senior ML engineers to improve calibration, model monitoring, experimentation, and production feedback loops
• Debug model-quality issues related to feature quality, label quality, sparse conversions, attribution noise, delayed feedback, data freshness, or online/offline metric mismatch
• Collaborate with performance advertising signal engineers to use model-ready features, labels, attribution windows, and feedback loops effectively
• Partner with engineering teams to deploy models into production decisioning systems and monitor their impact
• Work cross-functionally with product, analytics, platform, and business teams to understand campaign performance problems and translate them into ML work

Requirements

• 3+ years of experience building machine learning, data science, ranking, prediction, recommendation, optimization, or large-scale data systems
• Strong understanding of core ML concepts such as supervised learning, classification, regression, ranking, calibration, feature engineering, model evaluation, and experimentation
• Experience training, evaluating, and improving production-oriented ML models
• Experience working with large datasets using SQL, Spark, Python, or similar tools
• Strong programming skills in Python, Java, Scala, Go, C++, or similar languages
• Ability to reason about model quality, data quality, business impact, and production tradeoffs
• Comfort working with ambiguous data problems and iterating through analysis, modeling, experimentation, and production deployment
• BS or MS degree in Computer Science, Machine Learning, Statistics, Mathematics, Engineering, or a related technical field, or equivalent practical experience

Tech stack

Benefits