Redwood City, United States; Remote, United States
Full job description
Senior Principal Machine Learning Engineer role focused on building large-scale machine learning models and optimization systems for performance advertising on PubMatic’s Activate platform. Responsibilities include developing models for campaign optimization, prediction, ranking, bidding, forecasting, and calibration to improve advertiser outcomes. Requires 10+ years of experience in production ML systems, strong ML fundamentals, and engineering skills in Python, Java, SQL, Spark, TensorFlow, PyTorch, and XGBoost. Must have experience with CTR/CVR prediction, conversion modeling, bid optimization, and performance advertising goals such as CTR, VCR, CPC, CPA, and ROAS. The role is hybrid based in Redwood City, CA, with remote options for the right candidate. Benefits include paid leave, healthcare, commuter benefits, unlimited discretionary time off, and stock units. Salary range is $260,000 to $330,000 USD.
What you'll do
Build and improve machine learning models for campaign optimization, prediction, ranking, bidding, forecasting, and calibration
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Redwood City, United States; Remote, United States
Full-time
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
PythonJavaSQLSparkTensorFlowPyTorchXGBoost
Benefits
Paid leave programsPaid holidaysHealthcare, dental and vision insuranceDisability and life insuranceCommuter benefitsPhysical and financial wellness programsUnlimited discretionary time off (DTO) in the USReimbursement for mobileFully stocked pantriesIn-office catered lunches 5 days per weekBonusRestricted stock units
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