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Algorithms Engineer

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Remote from
USA
Salary
USD 129,302–149,302 / yr
Employment
Full Time
Experience
Open level
Published
Apply before
27 Oct 2026
Listing views
46
Application actions
3
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AI Summary

The role, at a glance.

QuinStreet is seeking an Algorithms Engineer to develop analytical insights and improve business and internal KPIs through statistical, machine-learning, and optimization methods. The role combines data analysis with production software engineering, including implementation of algorithms, models, and heuristics. The engineer will work with cross-functional teams to address technical and business challenges and improve data-collection and analysis standards. Candidates need a related bachelor’s degree, two years of relevant experience, and expertise in applied ML, scalable computing, experimentation, optimization, and system design.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

4/5
EasyHard

Pace & Pressure

4/5
RelaxedFast-paced

Autonomy Level

3/5
GuidedFull ownership

Communication Load

4/5
IndependentCollaborative
AI insightThis role requires practical depth across machine learning, statistics, optimization, scalable computing, and production-quality software development. Success also depends on translating ambiguous business KPIs into reliable models, experiments, and actionable insights.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job medianMarket rate
$139,302
US market range$120k–$165k
AI insightThe disclosed annual salary range is $129,301.92 to $149,301.92 USD, with a midpoint of $139,301.92. This is broadly competitive for an early-career to mid-level algorithms or applied machine-learning engineer in the US, though comparable roles in high-cost California markets may extend to approximately $120,000 to $165,000 depending on technical depth and total compensation.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
Describe a machine-learning solution you moved from experimentation into production.

I would explain the business problem, data preparation, model-selection process, evaluation metrics, and deployment architecture. I would also cover monitoring for data drift and model performance, along with the measurable impact on the relevant KPI.

How would you identify and investigate an unexpected decline in a campaign-performance KPI?

I would first validate the metric and data pipeline, then segment results by meaningful dimensions such as source, audience, device, product, and time period. I would use statistical tests and trend analysis to isolate significant changes, form hypotheses, and validate corrective actions through controlled experiments where possible.

When would you use an optimization approach instead of a predictive machine-learning model?

I would use predictive modeling to estimate outcomes such as conversion probability or expected value. I would use optimization when the task requires selecting the best action under objectives and constraints, such as allocating budget, routing traffic, or maximizing expected performance using model predictions as inputs.

How do you design a trustworthy A/B test for a new matching or ranking algorithm?

I would define the primary KPI, guardrail metrics, target population, randomization unit, and required sample size before launch. I would monitor experiment integrity, account for statistical significance and practical effect size, and review potential segment-level impacts before deciding whether to roll out the change.

What practices help ensure that analytical code is production quality?

I use clear interfaces, version control, automated tests, code review, reproducible data transformations, and documented assumptions. I also build observability through logging, performance monitoring, data-quality checks, and rollback plans so that algorithm behavior can be maintained safely over time.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
Opportunity details

About this role.

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.

QuinStreet, Inc. seeks Algorithms Engineer in Foster City, CA.

Duties: Generate analytical insights from data and identify opportunities for improvement in the internal and business KPIs. Apply statistical and Machine Learning tools to improve our ability to identify, analyze, and interpret trends or anomalies and alerts. Produce well engineered software components to implement algorithms, models, and heuristics as directed by manager. Work with teams to understand technical and business challenges and contributes towards defining and improving standards of data collection and analysis to inform modeling and analysis, as well as such other duties as are assigned to you by your manager.

Requires a Bachelor’s degree in Computer Science or a related field plus 2 years of experience. Additionally, requires 2 years of experience with: Applied machine learning, statistical learning, mathematical optimization and scalable computing; Supervised and unsupervised learning on large datasets; Building production level code; Computer Science fundamentals and programming; System design; optimization; experimental design. Telecommuting is permitted.

40 hours/week, $129,301.92 – $149,301.92 per year. Must also have authority to work permanently in the U.S. Applicants who are interested in this position may apply https://www.jobpostingtoday.com/ Ref # 37012.

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.

Please see QuinStreet’s Employee Privacy Notice here.

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