Amazon.com Services LLC
Direct employer career site

Applied Scientist, Amazon Ads

Pay
$171,600 – $222,200 per year
Location
San Francisco, CA
Listed
3 days ago

About the role

Amazon Ads runs one of the largest real-time decision systems in the world. To meet advertiser goals, we process >4 million ad requests per second, and this team decides which of them are worth the compute. We mine a petabyte-scale stream of traffic to tell in-demand, valuable requests apart from wasteful ones, filtering out the noise and forwarding only what brings value to advertisers, all within milli-seconds. Our solutions save millions in compute costs and drive long-term growth for Amazon's Retail and Marketplace businesses.

Your impact and career growth

Amazon Ads is investing heavily in AI and ML, creating room for scientists to innovate and make their mark. As an Applied Scientist, you will own the science for well-defined supply-optimization problems and take your work end to end, from framing the approach through experimentation and into production with measured business impact.

  • You will partner closely with engineers to productionize your models and with product managers to tie your science to customer and advertiser outcomes.
  • You will share your findings, begin to mentor teammates, and grow your influence on the team's technical direction over time.
  • This is your opportunity to do high-rigor applied science in one of the fastest-growing businesses at Amazon.

Key job responsibilities

As an Applied Scientist in Amazon Ads, you will

  • Conduct hands-on data analysis and build large-scale ML models and pipelines for supply optimization.
  • Take your models end to end, from proof of concept through experimentation, optimization, and deployment into production, working closely with software engineers to productionize them and with product managers and fellow scientists along the way.
  • Design and run A/B experiments and causal analysis to validate hypotheses, draw insights from petabyte-scale data, and measure real business impact.
  • Research and apply advanced ML, including generative AI and large language models, to demand prediction and traffic filtering.
  • Drive end-to-end optimization projects that tackle ambiguous problems at massive scale, developing scalable, efficient processes for model development, validation, and deployment that optimize traffic monetization while maintaining customer experience.

Basic Qualifications

  • 3+ years of building models for business application experience
  • PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
  • Experience programming in Java, C++, Python or related language
  • Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

Preferred Qualifications

  • Experience using Unix/Linux
  • Experience in professional software development

Additional information

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If the country/region you're applying in isn't listed, please contact your Recruiting Partner.

Pay and benefits

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave.

USA, CA, San Francisco - 171,600.00 - 222,200.00 USD annually

Why you'll love this role

The scientific challenge here is scale and signal: large-scale ML on enormous, high-velocity data, optimization and auction dynamics, and causal measurement of what actually moves the business. Generative AI and LLMs are tools you reach for when they help, not the point of the work.

The impact also reaches beyond Amazon: we donated the Dynamic Traffic Engine (DTE) to the IAB Tech Lab as an open-source framework that lets demand-side platforms signal which bid requests they value, so supply-side platforms send fewer wasteful ones and QPS waste drops across the whole programmatic supply chain.

We are a highly motivated, collaborative team with an entrepreneurial spirit and a broad mandate to experiment and innovate. You will work alongside talented engineers, product managers, and scientists in a culture that rewards bias for action, innovation, and experimentation. What makes this role unique is the combination of scientific rigor with real-world impact at a scale very few systems reach.

Read the original posting

From Amazon.com Services LLC's careers page, first listed on Rolev.ai on November 6, 2026. Rolev.ai is not the employer.

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Common questions

What is the salary for the Applied Scientist, Amazon Ads role at Amazon.com Services LLC?

The Applied Scientist, Amazon Ads posting at Amazon.com Services LLC states compensation of $171,600 – $222,200 per year. Final pay may depend on location, experience, and interview evaluation on Amazon.com Services LLC's official career site.

Where is the Applied Scientist, Amazon Ads job at Amazon.com Services LLC located?

This Applied Scientist, Amazon Ads position at Amazon.com Services LLC is located in San Francisco, CA. Always check Amazon.com Services LLC's application page for specific state eligibility or relocation policies.

How do I apply for the Applied Scientist, Amazon Ads position at Amazon.com Services LLC?

This listing links directly to Amazon.com Services LLC's application page. You can apply directly on Amazon.com Services LLC's site, or use Rolev.ai to check your resume fit, generate a zero-hallucination tailored resume and cover letter for this posting, and autofill the application form in 1 click with the Rolev.ai Chrome extension. You always review the form and press Submit yourself.