PubMatic
Senior/Machine Learning Engineer - Performance Optimization
Redwood City, California, USA · Senior · full-time
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About the role
- This role is focused on applying machine learning, data analysis, feature engineering, model training, experimentation, and production ML techniques to improve advertiser outcomes across performance advertising goals such as CTR, VCR, CPC, CPA, and ROAS.
- Work with large-scale datasets from auctions, impressions, clicks, video events, conversions, users, context, inventory, campaigns, and marketplace feedback.
- Analyze model performance across offline metrics, online experiments, campaign outcomes, and business KPIs.
Responsibilities
- Develop and improve features, training datasets, labels, and evaluation workflows for performance advertising models.
- 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.
Requirements
- Ability to reason about model quality, data quality, business impact, and production tradeoffs.
Nice to have
- Experience in ads, search, recommendations, marketplaces, e-commerce, fintech, pricing, forecasting, bidding, or real-time optimization systems.
- Experience with CTR/CVR prediction, conversion modeling, campaign optimization, bid optimization, forecasting, calibration, or user-value modeling.
- Familiarity with programmatic advertising, ad serving, attribution, pacing, identity, performance advertising, or real-time bidding.
- Experience with TensorFlow, PyTorch, XGBoost, LightGBM, Spark ML, or similar ML frameworks.
- Experience with A/B testing, online experimentation, model monitoring, or production ML observability.
- Experience working with sparse labels, delayed feedback, biased datasets, or noisy attribution.
- Experience working cross-functionally with product, engineering, analytics, or business stakeholders.
Skills
- Experience working with large datasets using SQL, Spark, Python, or similar tools.
- Strong programming skills in Python, Java, Scala, Go, C++, or similar languages.
Compensation
- Range $260,000-$330,000 USD
- New hires and current team members are typically compensated toward the middle of our pay range.
- Total Compensation Range $260,000-$330,000 USD
Benefits
- In addition to salary PubMatic also offers a bonus, restricted stock units, and a competitive benefits package.
- Build, train, evaluate, and improve machine learning models for prediction, ranking, campaign optimization, bidding, forecasting, and calibration.
- Strong understanding of core ML concepts such as supervised learning, classification, regression, ranking, calibration, feature engineering, model evaluation, and experimentation.
Equal opportunity
- 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 About PubMatic PubMatic is one of the world's leading scaled digital advertising platforms, offering more transparent advertising solutions to publishers, media buyers, commerce companies and data owners, allowing them to harness the power and potential of the open internet to drive better business outcomes.
- Founded in 2006 with the vision that data-driven decisioning would be the future of digital advertising, we enable content creators to run a more profitable advertising business, which in turn allows them to invest back into the multi-screen and multi-format content that consumers demand. #LI-HYBRID Compensation Disclosure In accordance with applicable law, the below salary range provided is PubMatic's reasonable estimate of the total compensation for this role.
- New hires and current team members are typically compensated toward the middle of our pay range.
This listing is sourced directly from PubMatic's careers page and normalized into a canonical job model.