PubMatic
Senior Principal Machine Learning Engineer - Optimization
Minnesota · Principal
Stay score
odds of building a lasting career here
Sponsors, but it's cap-subject — you still face the weighted lottery (~61% per draw at Level IV). Good if you win; have a cap-exempt backup on your list.
Lottery odds assume a STEM candidate.
Personalize to your clock →H-1B wage level
the lottery is wage-weighted — each level is one more entry
This range already reaches Level IV — the maximum four lottery entries.
DOL prevailing wage, 2026-27 wage year · Software Developers (15-1252) · Southeast Minnesota nonmetropolitan area. Wage level is derived by USCIS from the offered wage, occupation and worksite; the occupation shown is inferred from the job title.
Employer immigration record
from this employer's Department of Labor filings
Files H-1B transfers
Sourced from Department of Labor LCA, PERM and prevailing-wage disclosure data. Employer matching is by name, so figures may be split across an employer's legal entities. Absence of a filing means none appears in our copy of the data, not that none exists.
Community outcomes
No reports yet — be the first to help the next applicant.
About the role
- 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.
Responsibilities
- Develop models and algorithms that improve advertiser outcomes while balancing spend delivery, cost efficiency, campaign goals, marketplace dynamics, and system constraints.
- 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.
- 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.
- Experience building large-scale prediction or optimization systems in production.
Compensation
- $260,000-$330,000 USD
- 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.
Benefits
- We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status
- In addition to salary PubMatic also offers a bonus, restricted stock units, and a competitive benefits package.
- Build and improve machine learning models for campaign optimization, prediction, ranking, bidding, forecasting, and calibration.
- 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.
Equal opportunity
- PubMatic is proud to be an equal opportunity employer; we don't just value diversity, we promote and celebrate it.
- 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.
This listing is sourced directly from PubMatic's careers page and normalized into a canonical job model.