Senior Principal Machine Learning Engineer - Optimization

Das ist der Job

Design and improve CTR, CVR, VCR, CPA, ROAS, app-install, user-value, and campaign-performance models.

Darum lohnt es sich

Partner with engineering, product, analytics, and platform teams to translate model outputs into real-time decisioning systems.

Additional Information Return to Office: PubMatic employees throughout the globe have returned to our offices via a hybrid work schedule (3 days “in office” and 2 days “working remotely”) that is intended to maximize collaboration, innovation, and productivity among teams and across functions.

Benefits: Our benefits package includes the best of what leading organizations provide such as, paid leave programs, paid holidays, healthcare, dental and vision insurance, disability and life insurance, commuter benefits, physical and financial wellness programs, unlimited DTO in the US (that we actually require you to use!), reimbursement for mobile and fully stocked pantries plus in-office catered lunches 5 days per week.

New hires and current team members are typically compensated toward the middle of our pay range. In addition to salary PubMatic also offers a bonus, restricted stock units, and a competitive benefits package.

Role: Hybrid in Redwood City, CA. (Will consider Remote for the right candidate) Must Have: Experience building large-scale prediction or optimization systems PubMatic is the leading AI-powered ad tech company delivering measurable advertising performance through an intelligent, unified platform that connects buyers, publishers, data partners, and commerce media across CTV, mobile app, and omnichannel environments.

About the Role: We are looking for a Senior Principal Machine Learning Engineer to help build the next generation of performance optimization capabilities for PubMatic’s Activate platform.

This role is focused on applying machine learning, prediction, ranking, calibration, experimentation, and optimization techniques to improve campaign outcomes across performance advertising goals such as CTR, VCR, CPC, CPA, and ROAS.

The ideal candidate has strong ML fundamentals and experience building large-scale production models or optimization systems. 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.

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.

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. 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.

Preferred Experience: Experience in ads, search, recommendations, marketplaces, e-commerce, fintech, pricing, bidding, or real-time optimization systems. Experience with performance advertising goals such as CTR, VCR, CPC, CPA, ROAS, app install, retargeting, or user-value optimization.

Familiarity with real-time bidding, programmatic advertising, ad serving, attribution, pacing, identity, incrementality, or performance advertising. Experience with exploration/exploitation, counterfactual evaluation, uplift modeling, delayed-feedback modeling, or learning under biased logs.

Experience with model calibration, model observability, A/B testing, online experimentation, incrementality testing, or lift measurement. Experience working cross-functionally with product, engineering, analytics, and business stakeholders.

We’d love for you to have: 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.

Diversity and Inclusion PubMatic is proud to be an equal opportunity employer; we don’t just value diversity, we promote and celebrate it. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

Compensation Disclosure In accordance with applicable law, the below salary range provided is PubMatic’s reasonable estimate of the total compensation for this role. The actual amount may vary, based on non-discriminatory factors such as location, experience, knowledge, skills and abilities.

Total Compensation Range $260,000—$330,000 USD #J-18808-Ljbffr