Applied Scientist, Delivery Foundation Model

Amazon • Santa Clara, CA

20d

About The Position

Join the next science and engineering revolution at Amazon's Delivery Foundation
Model team, where you'll work alongside world-class scientists and engineers to
pioneer the next frontier of logistics through advanced AI and foundation
models.

We are seeking an exceptional Applied Scientist to help develop innovative
foundation models that enable delivery of billions of packages worldwide. In
this role, you'll combine highly technical work with scientific leadership,
ensuring the team delivers robust solutions for dynamic real-world environments.
Your team will leverage Amazon's vast data and computational resources to tackle
ambitious problems across a diverse set of Amazon delivery use cases.

The Delivery Foundation Model team combines ambitious research vision with
real-world impact. Our foundation models provide generative reasoning
capabilities required to meet the demands of Amazon's global Last Mile delivery
network. We leverage Amazon's unparalleled computational infrastructure and
extensive datasets to deploy state-of-the-art foundation models to improve the
safety, quality, and efficiency of Amazon deliveries. Our work spans the full
spectrum of foundation model development, from multimodal training using images,
videos, and sensor data, to sophisticated modeling strategies that can handle
diverse real-world scenarios. We build everything end to end, from data
preparation to model training and evaluation to inference, along with all the
tooling needed to understand and analyze model performance.

Requirements

• PhD, or Master's degree and 1+ years of CS, CE, ML or related field experience
• 4+ years of programming in Java, C++, Python or related language experience
• Experience building machine learning models or developing algorithms for business application
• 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

Nice To Haves

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

Responsibilities

• Design and implement novel deep learning architectures combining a multitude of modalities, including image, video, and geospatial data.
• Solve computational problems to train foundation models on vast amounts of Amazon data and infer at Amazon scale, taking advantage of latest developments in hardware and deep learning libraries.
• As a foundation model developer, collaborate with multiple science and engineering teams to help build adaptations that power use cases across Amazon Last Mile deliveries, improving experience and safety of a delivery driver, an Amazon customer, and improving efficiency of Amazon delivery network.
• Drive technical direction for specific research initiatives, ensuring robust performance in production environments.
• Develop and implement novel foundation model architectures, working hands-on with data and our extensive training and evaluation infrastructure
• Collaborate with fellow scientists in solving complex technical challenges, from trajectory planning to efficient multi-task learning
• Collaborate with fellow engineers in building scalable and reusable infra to support model training, evaluation, and inference
• Contribute to focused technical initiatives from conception through deployment, ensuring successful integration with production systems
• Drive technical discussions within the team and and key stakeholders
• Conduct experiments and prototype new ideas
• Make significant hands-on contribution to technical solutions

Benefits

• 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
• parental leave

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level