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Sr. Applied Scientist, Foundation Model Build, WW Sustainability

Amazon • United States • Added on Sep 9, 2026
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As a Senior Applied Scientist, you will lead the research and deployment of advanced AI systems designed to solve Amazon’s most complex environmental challenges. You will own the multi-year science roadmap, moving beyond simple model application to invent new scientific methods for foundation models and multimodal systems. Your work will involve establishing rigorous evaluation standards to ensure that AI-driven insights are trustworthy and production-ready at a global scale.

This role is unique because it applies cutting-edge machine learning to planetary-scale datasets, directly influencing how a global retail giant manages carbon estimation and climate risk. By bridging the gap between high-level AI research and tangible sustainability outcomes, you will set the scientific template for how technology drives corporate responsibility across billions of operations. It is a rare opportunity to use generative AI for measurable environmental impact.

This suits someone who is a hands-on scientific leader with a PhD or equivalent research experience in machine learning and a passion for building scalable, traceable AI solutions.

Job FAQs

What are the main missions and responsibilities of this role?

The primary mission is to define and execute a multi-year science roadmap that leverages AI to enhance sustainability decision-making. You will lead the full scientific lifecycle, from formulating hypotheses to deploying models in production environments. Key responsibilities include developing foundation models and multimodal systems specifically for environmental data. You will also be tasked with establishing rigorous evaluation criteria and governance models to ensure all strategic datasets are traceable and reproducible across the organization.

Key learning opportunities for this job

You will have the opportunity to work at the absolute frontier of AI and sustainability science, gaining experience in how foundation models can be adapted for non-traditional domains like climate risk. The role provides exposure to Amazon's internal research hubs and peer-review culture. Furthermore, you will deepen your expertise in large-scale distributed systems and data governance, learning how to maintain model performance and trust when operating at a scale involving billions of data points.

How does the ideal candidate look like (experience, skills)?

The ideal candidate is a seasoned scientist with a PhD or a Master's degree and significant applied research experience. You should possess deep technical fluency in Python, deep learning frameworks like TensorFlow or PyTorch, and a track record of building models for business applications. While sustainability expertise is not a prerequisite, you must be a rigorous scientific thinker capable of translating high-level, uncertain research questions into measurable, production-ready technical solutions that can withstand the demands of global operations.

Advice to stand out and make a successful application

Focus your application on your ability to bridge the gap between theoretical research and production deployment. Amazon values 'Scientific Leaders' who can not only build models but also define the standards by which those models are evaluated and governed. Highlight any experience you have with cross-disciplinary collaboration, particularly working alongside economists or environmental scientists. Demonstrating a 'Builder' mindset—where you create reusable methods rather than one-off scripts—will make your profile stand out to the hiring team.

What aspects of the company's sustainability is this role likely to focus on?

This role focuses on the technical engine of sustainability. You will likely work on projects related to product-level carbon footprinting, supply chain traceability, and climate-risk monitoring across Amazon's massive physical infrastructure. By building trustworthy data foundations, you enable the company to move from estimation to evidence-based environmental management. This ensures that every sustainability claim is backed by traceable data and rigorous scientific modeling.

What are the main challenges someone in this role might face?

One of the greatest challenges is the sheer complexity and noise of global supply chain data. Harmonizing disparate datasets into a unified foundation model requires significant architectural foresight and technical persistence. You may also face the challenge of balancing scientific rigor with business velocity. Ensuring that a model is scientifically 'correct' while meeting the fast-paced deployment needs of a global retail operation requires careful prioritization and stakeholder management.

How could a typical day look like for someone in this position?

A typical day involves a mix of deep-dive research and cross-functional synchronization. You might spend the morning writing code for foundation model adaptation and the afternoon reviewing science roadmaps with economists and engineers. You will likely participate in technical reviews, providing mentorship to junior scientists and refining evaluation benchmarks. Your day is spent ensuring that every piece of the scientific pipeline, from data ingestion to model output, is robust and scalable.

What are the opportunities for professional growth and development in this role?

Growth in this role is driven by high-visibility impact across the entire World Wide Sustainability organization. Success means setting the template for AI-driven science at Amazon, which can lead to principal scientist positions or senior leadership roles. There are also opportunities for external thought leadership through publications and participation in global sustainability and AI conferences, establishing you as a pioneer in the emerging field of Sustainability AI.

The main stakeholders you might be interacting with

You will interact frequently with applied scientists, economists, and environmental researchers to ensure your models are grounded in scientific reality. Collaboration with engineering teams is essential for production deployment. Additionally, you will influence senior product leaders and sustainability executives, helping them understand the scientific possibilities and limitations of AI as they set the company's long-term environmental strategies.

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