
Applied Scientist, Amazon Ads
San Francisco, CAJob$172–222K/yrSeen 2w agoSeen in employer's feed 5 days ago
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Job overview
Amazon Ads builds real-time systems that filter advertising requests and optimize supply at massive scale. The Applied Scientist owns well-defined supply-optimization problems end to end, from framing and experimentation through production deployment and measured business impact. The role partners with engineers, product managers, and scientists, applying rigorous machine learning and causal measurement to improve traffic monetization while maintaining customer experience.
Skills & qualifications
Skills
Qualifications
Benefits
Full job description
Description
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.
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.
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.
Learn more about Amazon Ads: https://advertising.amazon.com/
Key job responsibilities
As an Applied Scientist in Amazon Ads, you will:
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Conduct hands-on data analysis and build large-scale ML models and pipelines for supply optimization.
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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.
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Design and run A/B experiments and causal analysis to validate hypotheses, draw insights from petabyte-scale data, and measure real business impact.
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Research and apply advanced ML, including generative AI and large language models, to demand prediction and traffic filtering.
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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
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3+ years of building models for business application experience
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PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
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Experience programming in Java, C++, Python or related language
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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
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Experience using Unix/Linux
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Experience in professional software development
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
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. Learn more about our benefits at https://amazon.jobs/en/benefits .
USA, CA, San Francisco - 171,600.00 - 222,200.00 USD annually
How this job compares
Set against the 1,075 open Applied Scientist jobs on Olive Jobs, counted November 3, 2026.
- Pay
- This posting$172–222K/yr
- California median$172–260K/yr173 salary ranges
- US median$158–264K/yr1,036 salary ranges
This range's midpoint, $197K, is 13% below the California median midpoint ($226K) — in the middle half of Applied Scientist salary ranges in California.
- Openings
- Olive Jobs lists 192 open Applied Scientist jobs in California and 1,075 across the US. Under 1% of them nationwide are listed as remote or flexible.
- Experience
- This posting asks for at least 3 years of experience; Applied Scientist postings that state a minimum ask for 5 years at the median (918 postings).
- Education
- 582 of the 1,075 open Applied Scientist jobs (54%) ask for a bachelor's degree or higher.
Counted across the open, public US postings on Olive Jobs on November 3, 2026. Pay medians are the middle values of the low ends and of the high ends of postings that list both ends of a range in the same cadence (a yearly salary or an hourly rate); a range's midpoint is halfway between its ends. Pay and experience figures resting on fewer than 20 postings are left out.
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