Senior Applied Scientist, Delivery Foundation Model

Job description

Description
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 Senior 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.

Key job 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.
- Guide technical direction for specific research initiatives, ensuring robust performance in production environments.
- Mentor fellow scientists while maintaining strong individual technical contributions.

A day in the life
As a member of the Delivery Foundation Model team, you’ll spend your day on the following:
- Develop and implement novel foundation model architectures, working hands-on with data and our extensive training and evaluation infrastructure
- Guide and support fellow scientists in solving complex technical challenges, from trajectory planning to efficient multi-task learning
- Guide and support fellow engineers in building scalable and reusable infra to support model training, evaluation, and inference
- Lead 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
- Mentor team members while maintaining significant hands-on contribution to technical solutions

About the team
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.

Join us if you're excited about pushing the boundaries of what's possible in logistics, working with world-class scientists and engineers, and seeing your innovations deployed at unprecedented scale.
Basic Qualifications
- PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field, or Master's degree and 10+ years of industry or academic research experience
- 5+ years of building machine learning models or developing algorithms for business application experience
- Proficient with Data, experience with SQL and Spark
- Expert coders comfortable working in production environments using Python, C++ or other languages
- Strong publication record at top-tier conferences (NeurIPS, ICML, ICLR, CVPR, ICCV, RSS, CoRL) OR Demonstrated experience in applying machine learning innovation in industry- Experience mentoring junior scientists / engineers.
Preferred Qualifications
- Experience building foundation models for industry or research
- Experience designing multi-modal model architectures
- Experience building models for motion prediction, e.g. autonomous driving - Track record of successful production ML deployments
- Experience with large-scale distributed environments for ML training and inference
- History of impactful first-author publications at major conferences

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

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.

Our compensation reflects the cost of labor across several US geographic markets.

The base pay for this position ranges from $150,400/year in our lowest geographic market up to $260,000/year in our highest geographic market.

Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience.

Amazon is a total compensation company.

Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits.

For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits .

This position will remain posted until filled.

Applicants should apply via our internal or external career site.

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