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Remote Machine Learning Engineer Jobs in Pleasanton, CA

Senior Machine Learning Engineer

San Jose, CA ยท Remote

$229K - $367K/yr

We're looking for seasoned engineers with machine learning backgrounds to support this mission. Examples of problems include improving ad relevance, inferring demographics, optimizing yield, and more.

Senior Machine Learning Engineer

Mountain View, CA ยท On-site +1

$123K - $169K/yr

We're looking for a Senior Machine Learning Engineer to lead the development of these foundational AI systems within the Unity engine, empowering creators to build smarter, more responsive in-game ...

About The Opportunity Building machine learning systems for risk at a global crypto exchange is fundamentally different from conventional ML engineering. The data spans on-chain activity, fiat ...

Showing results 21-40

Remote Machine Learning Engineer information

See Pleasanton, CA salary details

$35.1K

$143.3K

$215.3K

How much do remote machine learning engineer jobs pay per year?

As of Aug 7, 2026, the average yearly pay for remote machine learning engineer in Pleasanton, CA is $143,307.00, according to ZipRecruiter salary data. Most workers in this role earn between $113,000.00 and $172,500.00 per year, depending on experience, location, and employer.

What are some typical challenges faced by remote machine learning engineers, and how are they addressed?

Remote Machine Learning Engineers often face challenges such as coordinating across different time zones, ensuring smooth communication with team members, and accessing large datasets or secure environments remotely. Organizations commonly address these by using robust collaboration tools (like Slack, GitHub, and Jira), establishing clear documentation, and setting regular virtual meetings to maintain alignment. Many companies also provide secure remote environments or VPN access for handling sensitive data and code. Proactive communication and organized workflows help mitigate these challenges, enabling engineers to remain productive and connected to their teams.

What are the key skills and qualifications needed to thrive as a remote machine learning engineer?

To thrive as a Remote Machine Learning Engineer, you need a strong background in computer science, mathematics, and experience with machine learning algorithms, typically supported by a relevant degree and prior project work. Proficiency with programming languages like Python, machine learning frameworks such as TensorFlow or PyTorch, and familiarity with cloud computing platforms is crucial, and certifications like AWS Certified Machine Learning can enhance your profile. Excellent communication, self-motivation, and time-management skills are also essential for collaborating across remote teams and meeting project goals. These combined technical and soft skills are vital for developing effective machine learning solutions while ensuring productivity and collaboration in a virtual work environment.

What is a remote machine learning engineer?

A Remote Machine Learning Engineer designs, develops, and deploys machine learning models while working from a remote location. They preprocess data, train and optimize models, and integrate them into production systems. Their role often involves collaborating with data scientists, software engineers, and stakeholders to solve complex problems using AI. Strong programming skills in Python, experience with ML frameworks like TensorFlow or PyTorch, and cloud computing knowledge are essential. Remote ML engineers must also communicate effectively and manage their time efficiently to work asynchronously with teams.

What are the most commonly searched types of Machine Learning Engineer jobs in Pleasanton, CA? The most popular types of Machine Learning Engineer jobs in Pleasanton, CA are:
What are popular job titles related to Remote Machine Learning Engineer jobs in Pleasanton, CA? For Remote Machine Learning Engineer jobs in Pleasanton, CA, the most frequently searched job titles are:
What job categories do people searching Remote Machine Learning Engineer jobs in Pleasanton, CA look for? The top searched job categories for Remote Machine Learning Engineer jobs in Pleasanton, CA are:
What cities near Pleasanton, CA are hiring for Remote Machine Learning Engineer jobs? Cities near Pleasanton, CA with the most Remote Machine Learning Engineer job openings:
Infographic showing various Remote Machine Learning Engineer job openings in Pleasanton, CA as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% Remote job distribution, with an average salary of $143,307 per year, or $68.9 per hour.

Senior Principal Machine Learning Engineer - Optimization

PubMatic

Redwood City, CA โ€ข On-site, Remote

$153K - $211K/yr

Full-time

Medical, Dental, Vision, Life, PTO

Re-posted 14 days ago


Job description

Role: Hybrid in Redwood City, CA. (Will consider Remote for the right candidate)

Must have:ย 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.ย 

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:ย 

    • 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.ย 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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