Join Our Team as a Principal Applied Scientist in AI for Worldwide Sustainability

Be part of Amazon's cutting-edge sustainability initiatives, driving environmental and social advancements to support our long-term global sustainability strategy. At Amazon, our mission is to be Earth's most customer-centric company, and to achieve this, we need exceptionally talented, bright, and driven individuals.

About Worldwide Sustainability (WWS)

The Worldwide Sustainability (WWS) organization leverages Amazon’s scale and speed to build a more resilient and sustainable company. We manage global social and environmental impacts, delivering solutions that promote sustainability for our customers, businesses, and the world.

About Sustainability Science and Innovation (SSI)

The Sustainability Science and Innovation (SSI) team within WWS uses multi-disciplinary expertise in science, analytics, economics, statistics, machine learning, product development, and engineering. Our mission is to develop and evaluate the innovations required by Amazon, its customers, and partners to meet long-term sustainability goals.

Position: Principal Applied Scientist

We are seeking a Principal Applied Scientist skilled in Machine Learning (ML), Artificial Intelligence (AI), and Large Language Models (LLMs) with a hands-on approach to complex innovations. Lead in conceptualizing, building, and launching impactful models and solutions for sustainability and climate goals.

Key Responsibilities

• Develop web-scale sustainability-specific data foundations aligned with impact areas and sustainability goals.
• Create models to measure environmental and economic impacts at scale.
• Automate complex ESG tasks and develop reasoning mechanisms for multi-view decarbonization plans and multi-objective optimization models.
• Design models to monitor and ensure high-integrity forest carbon credits.

Collaborative and Leadership Roles

Collaborate with business leaders, scientists, and engineers to incorporate sustainability nuances into data foundations, AI models, and applications. Utilize advanced techniques like data indexing, validation metrics, model distillation, and customized loss functions, embedding AI/ML solutions into existing systems. Define AI sustainability research directions and conduct experiments, translating research into practice.

About the Team

Diverse Experiences

WWS values diverse experiences. We encourage candidates to apply, even if they don’t meet all qualifications. Whether your career is just starting, non-traditional, or includes alternative experiences, we want to hear from you.

Inclusive Team Culture

We foster an inclusive culture where employee-led affinity groups empower us to embrace our differences. Events like Conversations on Race and Ethnicity (CORE) and AmazeCon inspire us to continuously grow our inclusive culture.

Mentorship & Career Growth

Amazon is dedicated to being Earth's best employer, offering knowledge-sharing, mentorship, and career-advancement resources to help you grow as a professional.

Work/Life Balance

We prioritize work-life harmony, providing flexible work hours and arrangements to ensure success at work and at home. When supported, we achieve greater success together.

Locations

We are open to hiring in: Arlington, VA | New York City, NY | San Francisco, CA | Seattle, WA

Basic Qualifications

• Master’s Degree in Computer Science, Sustainability, Environmental Sciences, or a related field.
• Extensive research experience in generative AI and machine learning.
• 10+ years of hands-on experience in NLP, machine learning, or related fields.
• Proven experience building, deploying, and operationalizing models and algorithms, particularly in sustainability areas.
• Experience with diverse data sources such as text, time-series, tabular, and images.
• Mentorship experience guiding junior scientists and engineers.

Preferred Qualifications

• PhD in Computer Science, sustainability, environmental sciences, or related fields.
• 10+ years of experience with large-scale AI model development and deployment.
• Expertise in generative models, LLMs, or multi-modal systems, especially in sustainability applications.
• Proficiency in Python and deep learning frameworks like TensorFlow or PyTorch.
• Effective communication skills across multiple teams and organizations.
• Experience publishing research in top-tier peer-reviewed conferences or journals.