Machine Learning Engineer II, Workforce Solutions

The Workforce Solutions Analytics and Tech team is actively seeking candidates who are interested in solving challenging problems using latest developments in Large Language Models and Artificial Intelligence (AI). We are looking for a talented AI and Machine Learning (ML) engineer with a solid background in the design and development of scalable AI and ML systems and services, deep passion for building ML-powered products, a proven track record of executing complex projects, and delivering high business and customer impact. Your contributions will be instrumental to tackle staffing challenges within Amazon's warehouses.
As a member of our team, you'll work on cutting-edge projects that directly impact over a million Amazon associates. This role will provide exposure to state-of-the-art innovations in AI/ML systems (including GenAI). Technologies you will have exposure to, and/or will work with, include AWS Bedrock, Amazon Q, SageMaker, and Foundational Models such as Anthropic’s Claude / Mistral, among others.

The types of initiatives you can expect to work but not limited to include:
- Developing personalized recommendation systems.
- Building AI Assistant tools that have cross-Amazon user adoption.


Key job responsibilities
- Design, implement, and productionize AI/ML models by working very closely with scientists on the team.
- Develop ML/LLM workflows and end-to-end pipelines for data preparation, training, deployment, monitoring, etc., and ensure a high bar for the quality of architecture and design of our AI ML systems and data infrastructure
- Leverage AWS AI services and other internal / publicly available external tools & services to accelerate our AI investments
- Detail-oriented, always backs up ideas with facts. Understands complex application data flows and bridge the gap between technical and business app requirement
- Identify state of the art models / solutions to enable new capabilities for code migration and code testing, drive down tech debt and increase operational efficiency
- Share expert knowledge in performance, large scale distributed system scalability, system architecture, and engineering best practices
- Provide thought leadership and hands-on support in selecting, defining, training and fine-tuning Large Language Models (LLMs), prompt engineering, and other GenAI efforts.

We are open to hiring candidates to work out of one of the following locations:

Arlington, VA, USA | Austin, TX, USA | Bellevue, WA, USA | Nashville, TN, USA | Phoenix, AZ, USA | Seattle, WA, USA | Tempe, AZ, USA