Applied Scientist, Foundation Model Build

- 3+ years of building machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning
- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.

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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, WA, Seattle - 167,100.00 - 226,100.00 USD annually
Build AI systems that help Amazon make better sustainability decisions at global scale. Our research questions require more than applying an existing model: they require new scientific methods, trustworthy data foundations, and a path from research hypothesis to production deployment.

Sustainability Science and Innovation (SSI) is Amazon's applied research hub for environmental impact. We bring together applied scientists, environmental scientists, economists, and engineers to develop and scale solutions across carbon, water, waste, climate risk, and responsible supply chains—from early hypothesis to production deployment at Amazon scale.

SSI is seeking a Senior Applied Scientist to own a research agenda at the intersection of artificial intelligence, data, and sustainability. The role will define the science roadmap, formulate and test hypotheses, establish evaluation standards, and lead solutions from early experimentation through production deployment. Working closely with economists, environmental scientists, engineers, and product leaders, the Senior Applied Scientist will determine which scientific and technical approaches can produce decision-ready results at Amazon scale.

The work may include large language models, multimodal models, retrieval-augmented generation, foundation-model adaptation, and other modern machine-learning methods, selected according to the scientific problem rather than applied as ends in themselves. The role will also define how strategic models and datasets are discovered, evaluated, ingested, harmonized, governed, and maintained, because trustworthy AI depends on traceable evidence, stable data contracts, and reproducible evaluation. Applications may include product-level carbon estimation, climate-risk monitoring, and responsible-supply-chain assessment.

This role is distinctive because Amazon’s operational scale creates scientific problems that few organizations can study, with unique access to global-scale sustainability data. You'll leverage this unique access to establish scientific methods, governance models, and evaluation standards that can scale across multiple programs. This role shapes not just what problems we solve, but how we solve them rigorously setting a template for AI-driven sustainability science across Amazon's global operations.

Candidates do not need prior expertise in sustainability or climate science. The role requires a hands-on scientific leader who can develop rigorous AI and machine-learning methods, work effectively across disciplines, and translate uncertain research questions into measurable, production-ready solutions.

Key job responsibilities
• Own the research agenda and multi-year science roadmap for AI-enabled sustainability solutions.
• Develop and evaluate modern AI and machine-learning methods, including foundation models, multimodal models, retrieval-augmented generation, and model adaptation.
• Establish ex ante evaluation criteria, benchmarks, and launch thresholds that distinguish promising prototypes from production-ready methods.
• Lead the full scientific lifecycle, from problem formulation and experimentation through production deployment and post-launch measurement.
• Define the architecture and governance required to make strategic models and datasets discoverable, traceable, reproducible, and reusable.
• Influence senior science, engineering, product, and sustainability stakeholders across organizational boundaries.
• Mentor scientists and raise the scientific standard through technical reviews, publications, and reusable methods.

About the team
Diverse Experiences:
World Wide Sustainability values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.

Inclusive Team Culture:
It’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (inclusive diversity) conferences, inspire us to never stop embracing our uniqueness.

Mentorship & Career Growth:
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.

Work/Life Balance:
We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.