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Programmatic Advertising Remote Jobs in Santa Clara, CA

All JobsProgrammatic Advertising JobsProgrammatic Advertising Remote JobsProgrammatic Advertising Remote Jobs in Santa Clara, CA

Programmatic Advertising Remote information

See Santa Clara, CA salary details

$17

$26

$41

How much do programmatic advertising remote jobs pay per hour?

As of Sep 5, 2026, the average hourly pay for programmatic advertising remote in Santa Clara, CA is $26.32, according to ZipRecruiter salary data. Most workers in this role earn between $20.62 and $29.90 per hour, depending on experience, location, and employer.

What is programmatic advertising in a remote job context?

Programmatic advertising refers to the automated buying and selling of digital advertising space using software and data-driven strategies. In a remote job context, professionals manage campaigns, analyze data, and optimize ad performance from anywhere, collaborating with teams and clients virtually. This role typically requires proficiency with programmatic platforms, understanding of digital marketing, and strong communication skills to coordinate across time zones. Remote programmatic advertising jobs offer flexibility while leveraging technology to streamline ad delivery and maximize ROI for clients.

What are the key skills and qualifications needed to thrive as a programmatic advertising specialist in a remote role?

To excel as a Programmatic Advertising Specialist in a remote setting, you need a strong grasp of digital marketing concepts, data analysis, and experience with programmatic ad buying, often backed by a relevant degree or certifications such as Google Display & Video 360. Familiarity with demand-side platforms (DSPs), ad servers, analytics tools, and industry certifications is typically required. Exceptional communication, proactive problem-solving, and self-management skills help you collaborate effectively with distributed teams and clients. These competencies ensure campaigns are optimized, goals are met efficiently, and clients receive high ROI in the fast-evolving digital advertising landscape.

What are some common challenges faced by remote professionals in programmatic advertising, and how can they be managed?

Remote professionals in programmatic advertising often face challenges such as coordinating with cross-functional teams across time zones, staying updated with rapidly evolving ad technologies, and maintaining clear communication with clients and partners. To manage these challenges, it's helpful to establish regular check-ins, use collaborative project management tools, and proactively share updates and insights with team members. Additionally, dedicating time for continuous learning and staying engaged with industry trends is crucial for success in a remote environment.

What is the difference between Programmatic Advertising Remote vs Programmatic Media Buyer?

AspectProgrammatic Advertising RemoteProgrammatic Media Buyer
CredentialsExperience with ad platforms, certifications like IAB or Google AdsSimilar certifications, often required to operate ad platforms
Work EnvironmentRemote, digital workspace, collaborative toolsRemote or in-office, client-facing or agency setting
Industry UsageUsed across digital marketing agencies, brands, ad tech firmsCommonly employed in media agencies, brands, and ad tech companies

Programmatic Advertising Remote and Programmatic Media Buyer roles share similar credentials and work environments, often involving digital platforms and certifications. While Programmatic Advertising Remote emphasizes a remote, flexible setup, Programmatic Media Buyers focus on executing ad campaigns across various platforms. Both roles are integral to digital marketing and frequently overlap in skills and industry usage.

What are popular job titles related to Programmatic Advertising Remote jobs in Santa Clara, CA?

For Programmatic Advertising Remote jobs in Santa Clara, CA, the most frequently searched job titles are:

What job categories do people searching Programmatic Advertising Remote jobs in Santa Clara, CA look for?

The top searched job categories for Programmatic Advertising Remote jobs in Santa Clara, CA are:

What cities near Santa Clara, CA are hiring for Programmatic Advertising Remote jobs?

Cities near Santa Clara, CA with the most Programmatic Advertising Remote job openings:

Programmatic Advertising Remote jobs near you
Infographic showing various Programmatic Advertising Remote job openings in Santa Clara, CA as of July 2026, with employment types broken down into 86% Full Time, 10% Part Time, and 4% Contract. Highlights an 89% Physical, 3% Hybrid, and 8% Remote job distribution, with an average salary of $54,752 per year, or $26.3 per hour.

Senior Principal Machine Learning Engineer - Optimization

PubMatic

Redwood City, CA • On-site, Remote

$153K - $211K/yr

Full-time

Medical, Dental, Vision, Life, PTO

Re-posted 12 days ago

Job description

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

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.

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

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.

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