Senior Principal Machine Learning Engineer - Optimization

What you'll do

• Build and improve machine learning models for campaign optimization, prediction, ranking, bidding, forecasting, and calibration
• Develop models and algorithms that improve advertiser outcomes while balancing spend delivery, cost efficiency, campaign goals, marketplace dynamics, and system constraints
• Work on large-scale ML systems using signals from auctions, impressions, clicks, video events, conversions, users, context, inventory, campaigns, and marketplace feedback
• Design and improve CTR, CVR, VCR, CPA, ROAS, app-install, user-value, and campaign-performance models
• Develop bidding, pacing-aware optimization, ranking, exploration, and value-estimation approaches for performance advertising
• Improve model calibration, online/offline evaluation, experimentation, observability, and production feedback loops
• Reason through sparse conversions, delayed feedback, biased logs, cold-start campaigns, attribution noise, and online/offline metric mismatch
• Partner with performance advertising signal engineers to define model-ready features, labels, attribution windows, negative examples, training datasets, and online serving requirements
• Partner with engineering, product, analytics, and platform teams to translate model outputs into real-time decisioning systems
• Help evolve Activate from a media buying execution platform into a performance optimization platform
• Provide technical leadership and mentorship to engineers and applied scientists working on performance optimization problems

Requirements

• 10+ years of experience building production machine learning, ranking, recommendation, prediction, optimization, ads, marketplace, bidding, or pricing systems
• Strong understanding of supervised learning, ranking, calibration, causal thinking, experimentation, statistical evaluation, and model monitoring
• Experience building large-scale prediction or optimization systems in production
• Experience with CTR/CVR prediction, conversion modeling, bid optimization, value modeling, forecasting, calibration, or performance optimization
• Strong ability to reason about model quality, business impact, system constraints, production tradeoffs, and online performance
• Experience working with large-scale data and distributed ML workflows
• Strong engineering skills in Python, Java, SQL, Spark, TensorFlow, PyTorch, XGBoost, or similar technologies
• Ability to provide technical leadership across ambiguous, high-impact optimization problems
• BS, MS, or PhD in Computer Science, Machine Learning, Statistics, Mathematics, Engineering, or a related technical field

Tech stack

Benefits