Amazon’s North American Transportation Data Science & Analytics team is looking to develop agentic AI solutions to simulate, reason, and deliver insights across their complex transportation network to improve delivery speed and customer satisfaction.
Requirements
- 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
- Experience using Unix/Linux
- Experience in professional software development
- 3+ years of building models for business application experience
- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
Responsibilities
- Design, build, and deploy autonomous AI agents that support advanced analytics and experimentation workflows in transportation.
- Develop agents that can interact with APIs, query internal data/knowledge sources, and reason over structured and unstructured data.
- Collaborate with Data Scientists, BIEs, and Engineers to integrate agentic systems into existing analytical pipelines and business tools.
- Use agentic AI to simulate customer or system behavior and define new performance and quality metrics for delivery experience.
- Develop evaluation frameworks to measure agent performance, scalability, and business impact.
- Contribute to the development of shared infrastructure for agent development—such as reusable APIs, prompt libraries, and model registries.
- Partner with internal AWS teams to stay aligned with best-in-class tools and infrastructure for generative and agentic AI.
Other
- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- 3+ years of building models for business application experience
- Ability to work in a fast-moving, customer-obsessed environment
- Experience with data-driven decision-making and analytics
- Ability to collaborate with cross-functional teams
