Senior Machine Learning Engineer - Ranking

Powering Performance Marketplaces in Digital Media
QuinStreet is a pioneer in powering decentralized online marketplaces that match searchers and “research and compare” consumers with brands. We run these virtual- and private-label marketplaces in one of the nation’s largest media networks.
Our industry leading segmentation and AI-driven matching technologies help consumers find better solutions and brands faster. They allow brands to target and reach in-market customer prospects with pinpoint segment-by-segment accuracy, and to pay only for performance results.
Our campaign-results-driven matching decision engines and optimization algorithms are built from over 20 years and billions of dollars of online media experience.
We believe in:
The direct measurability of digital media.
Performance marketing. (We pioneered it.)
The advantages of technology.
We bring all this together to deliver truly great results for consumers and brands in the world’s biggest channel.
Job Category
Join us to shape how users discover and interact with our marketplace. You’ll advance algorithms for ordering, prioritization, and personalization. Ranking is a core component which helps users discover ads, balance user satisfaction, business goals, and system health.
Responsibilities
Design and improve ranking systems through rigorous measurement and experimentation; translate broad goals into clear metrics and deliver steady, validated gains.
Advance evaluation practices (clean test design, offline & online alignment) and help teams make evidence-based decisions.
Build high-value signals and features with reliable offline/online pipelines and robust monitoring. Incorporate content understanding signals such as text/image/metadata.
Make uncertainty-aware decisions; handle drift and calibration so models remain stable and trustworthy over time.
Partner with Engineering, Product, Business and Analytics to ship resilient solutions end-to-end.
Requirements
Advanced degree (MS/PhD) in CS, Statistics, or related field, with 3+ years post-PhD or 5+ years industry experience.
Strong foundations in applied ML, statistics, and optimization with demonstrated impact in ranking/recommendations.
Proficiency in Python and solid software engineering practices (testing, CI/CD).
Experience working with large-scale datasets, distributed systems, and latency-sensitive production ML.
Clear communication, cross-functional collaboration, and an ownership mindset.
Preferred
Demonstrated success in delivering production-grade ranking systems with measurable business impact.
Track record building feature/signals pipelines, feature stores, and observability for ML systems.
Depth in experimentation and metrics design, including large-scale A/B testing and variance reduction.
Familiarity with monitoring, calibration, concept drift detection, and adaptive or online learning.
Proficiency in SQL is desired.
Knowledge about Linear Algebra, Combinatorial optimization is desired.
Proficiency in scalable software design and development.
Exposure to multimodal signals (text/image/metadata) is a plus, not required.
The expected salary range for this position is $140,000 USD to $170,000 USD annually. This salary range is an estimate, and the actual salary may vary based on the Company’s compensation practices. The salary may be adjusted based on applicant's geographic location. The position is also eligible to receive performance bonus or commission and equity in the form of restricted stock units. This position is eligible to participate in the Company’s standard employee benefits programs, which currently include health care benefits; (2) retirement benefits; (3) the amount of paid days off (paid sick leave, parental leave, paid time off, or vacation benefits); (4) any other tax-reportable benefits.
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QuinStreet is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, national origin, pregnancy status, sex, age, marital status, disability, sexual orientation, gender identity or any other characteristics protected by law.
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