Software Development Engineer, AWS Marketplace & Partner Services
Amazon
- Location
- US, WA, Seattle
- Employment
- Full Time
- Work model
- On-Site
- Level
- Mid
- Posted
- 20h ago
Skills
About this role
The AWS Marketplace & Partner Services (AMPS) Science team seeks a Software Engineer to design and build the infrastructure and tooling behind our AI solutions, including LLM training pipelines, custom LLM model hosting, and AI agent monitoring and evaluation infrastructure. This role will be instrumental in transforming how AMPS customers and partners interact with our AI-powered products, including AMPS AI agents. In this role, you will work closely with Data Scientists, Applied Scientists, Data Engineers, other Software Engineers, Business Intelligence Engineers, Product Managers, and AMPS managers and leaders to build and scale the infrastructure and tooling that power our AI solutions. The ideal candidate thrives in an environment of practical application, demonstrating both technical excellence and business acumen. They should be passionate about collaboration and contributing to a culture of continuous learning and innovation. This role directly influences how thousands of AWS customers and partners as well as internal users and systems work, making it crucial for AWS Marketplace's growth and customer success. The position offers the opportunity to shape the future of AI-driven solutions while working with innovative technologies at AWS scale. Key job responsibilities - Design, develop, and maintain scalable infrastructure for large language model training pipelines, model hosting, and agent-based systems, employing best engineering practices and iterating from simple to complex solutions - Build and operate monitoring, evaluation, and observability frameworks to measure AI agent performance, reliability, and effectiveness in production - Develop reusable tooling, APIs, and automation that accelerate the team's ability to experiment, deploy, and iterate on ML/AI solutions - Contribute to a fast-paced, experimental environment by rapidly prototyping and operationalizing infrastructure that leverages the most recent advances in ML/AI - Collaborate with cross-functional teams—including Data Scientists, Applied Scientists, Data Engineers, Software Engineers, Product Managers, and Science and Engineering leaders—to translate scientific innovations into robust, production systems - Participate in research initiatives alongside scientists and engineers—contributing to experiments, prototyping, and co-authoring internal and external publications that advance the team's technical knowledge and industry presence. A day in the life We are at the forefront of developing and deploying AI/ML systems that serve multiple critical stakeholders: - AWS Customers: Through the AWS Marketplace, we build and support Discovery tools and AI agents that streamline cloud adoption and innovation. - AWS Partners: Via Partner Central, we offer advanced tools and insights to enhance collaboration and drive mutual growth. - Internal AWS Sellers: We equip our sales force with data-driven recommendations and AI tools to better serve our customers and partners. - Our primary objective is to accelerate cloud migrations and modernizations, fostering innovation for AWS customers while simultaneously supporting the growth and success of our extensive partner network. About the team AMPS' vision is to make AWS the most efficient, automated, and economical place for customers worldwide to find, buy, and deploy third-party software, data, and professional services. The AMPS Analytics and Science Engineering Team drives innovation through data science, machine learning research, and rigorous AI evaluation. We partner with other AMPS teams to improve discovery, recommendation, AI systems performance and evaluations, and partner success across AWS Marketplace. Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences,