Software Development Manager, Everyday Essentials Science

- 3+ years of engineering team management experience
- 7+ years of engineering experience
- 8+ years of leading the definition and development of multi tier web services experience
- Knowledge of engineering practices and patterns for the full software/hardware/networks development life cycle, including coding standards, code reviews, source control management, build processes, testing, certification, and livesite operations
- Experience partnering with product or program management teams
- Experience designing or architecting (design patterns, reliability and scaling) of new and existing systems
- Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, or a related field
- Experience in communicating with users, other technical teams, and senior leadership to collect requirements, describe software product features, technical designs, and product strategy
- Experience in recruiting, hiring, mentoring/coaching and managing teams of Software Engineers to improve their skills, and make them more effective, product software engineers

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.

null
Amazon's Everyday Essentials (EE) Science team is looking for a Software Development Manager to lead the engineering team that brings our science into production. Our team builds the intelligence behind how customers find, reorder, and discover everyday products - using Generative AI, large language models (LLMs), and machine learning to power recommendations, substitutions, subscriptions, personalization, and next-generation AI-powered shopping experiences across Amazon.



Key job responsibilities
• Lead and develop a team of software engineers, building individualized growth plans, coaching engineers toward their aspirations, and hiring to raise the bar.
• Own technical strategy and architecture for production ML systems, including real-time inference services, batch ML pipelines, and the data foundations underneath them.
• Build and scale production LLM systems, including model management, fine-tuning, retrieval, evaluation, and observability.
• Partner with applied scientists to take models from prototype to production, and build the experimentation, evaluation, and feedback infrastructure that shortens the path from idea to measured customer impact.
• Expose ML-powered intelligence through well-designed APIs and integrations that partner teams can adopt with minimal bespoke work.
• Drive operational excellence for production services, including availability, latency, on-call health, and cost efficiency.
• Foster an inclusive, high-performing culture where diverse perspectives thrive

· Integrate EE Science intelligence into Amazon's central systems and agentic experiences, exposing it through APIs and agent tools that partner teams can adopt with minimal bespoke work.

· Drive operational excellence for services that partner teams depend on, including availability, latency, on-call health, and cost efficiency.

· Foster an inclusive, high-performing culture where diverse perspectives thrive and work/life harmony is sustainable.



A day in the life
You'll lead a team of software engineers who work closely with applied scientists. Your team owns the production systems behind our science: real-time services and APIs, large-scale ML and LLM pipelines, the evaluation and feedback infrastructure that keeps our models improving, and the integrations that deliver our intelligence into core Amazon shopping experiences.

The problems are hard and the field is moving fast:
• How do you serve ML models at scale through APIs with the latency and reliability a shopping page demands?
• How do you take LLM-powered personalization from prototype to production, with model management, fine-tuning, retrieval, and monitoring for quality and cost at Amazon scale?
• How do you build a feedback loop that captures how customers respond to every recommendation and routes that signal back, so every model improves on its own?
• How do you build AI-powered systems that enhance the shopping experience and make them trustworthy enough to act on a customer's behalf?

The Impact You'll Have
Production ML at Amazon scale: You'll own the services that deliver our science to customers - from real-time APIs serving high-throughput traffic worldwide, to low-latency retrieval over large product catalogs.
LLM and GenAI systems: You'll build and run the platform that makes LLM work production-grade - including model management, fine-tuning, quality evaluation, and robust monitoring.
AI-powered shopping experiences: You'll help shape the next generation of intelligent shopping experiences, exposing ML capabilities through APIs that teams across Amazon can integrate into customer-facing products.
Broad reach: Your team's work powers experiences across multiple Amazon surfaces and marketplaces, so a single launch can reach customers at scale.


About the team
Our team transforms everyday essentials shopping through Generative AI and advanced machine learning. We build reusable intelligence about products and customers, and deliver it into the systems that reach customers across Amazon. The Bangalore team brings applied scientists and software engineers together in one organization, and partners closely with science teams in Seattle and Vancouver. We value curiosity, scientific rigor, and developing each team member's career