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Description

Amazon's Selling Partner Support handles tens of millions of contacts annually worldwide. The Titans Science team is transforming this experience by building AI agents that autonomously resolve seller issues, learn from every interaction, and continuously improve with minimal human intervention. These agents reason, remember, and adapt — from understanding the seller's context and selecting the right solution, to routing contacts optimally, automating resolution end-to-end, and augmenting associates with AI when human judgment is needed. We do this in deep partnership with multiple engineering and product partners.

We are looking for a Senior Applied Scientist who wants to work at the intersection of reinforcement learning, agentic architectures, and large-scale production systems. You will be directly connected to the problems sellers face every day, translating real customer pain into science solutions that operate at massive scale. You will frame ambiguous business challenges as tractable ML problems to shipping systems that measurably improve millions of seller interactions.

Key job responsibilities
- Own end-to-end research and development of RL-based agent improvement systems — from problem formulation through production deployment and impact measurement.
- Design novel approaches to preference learning, reward modeling, and policy optimization in the context of conversational agents operating over real-world tools and APIs.
- Build and maintain evaluation frameworks that measure agent quality across multiple dimensions: helpfulness, correctness, safety, and alignment with operational standards.
- Collaborate with a team of scientists that work on forefront of Natural Language Understanding, Optimization, Machine Learning and Statistics
- Partner with 10+ engineering teams to deploy models into production systems serving sellers worldwide.
- Publish research at top venues (NeurIPS, ICML, EMNLP, AMLC) — the complexity of our problems produces publishable work, and we actively support it.
- Raise the scientific bar through rigorous peer review, mentorship of junior scientists, and contribution to hiring.

A day in the life
You read the latest research papers and implement novel techniques by building rapid prototypes using AI-assisted coding tools, then taking what works from prototype to production. You collaborate closely with product managers and engineering teams to translate seller pain points into deployed science solutions. You influence leadership by bringing the state of the art to strategic decisions about where the organization invests, and you drive the science roadmap for your domain — identifying new research directions, proposing experiments, and making the case for what to build next. You mentor other scientists on the team, raising the bar on rigor and execution and get mentored by Principals across the org. Finally, you attend meetings with other Amazonians to stay connected to the seller experience by understanding the real problems sellers face so your models solve what actually matters.

About the team
Titans Science is a growing team of scientists building the AI that powers Amazon's seller support experience. We operate in across capabilities such as Agentic Systems, Knowledge Retrieval & Query Understanding, and Content Intelligence & Automation, each owning distinct problem spaces but sharing evaluation infrastructure and research insights. We work backwards from business problems, deeply understanding the problem space and domain, defining gold-standard datasets, success metrics, and guardrails. This lets us run parallel experiments, compare approaches rigorously, and ship the best Science models to production. We publish at internal conferences and external venues, and we actively invest in research that compounds over multiple product cycles.

The team sits in Seattle and operates with high autonomy. Scientists own their domains end-to-end, from problem framing through production deployment. We value speed over perfection, scientific rigor over polish, and experimentation over debate. We value diverse experiences. Even if you do not meet all of the preferred qualifications listed above, we encourage you to apply. The team fosters an inclusive learning culture where individual growth is a priority — you will find mentorship, knowledge-sharing, and career-advancing resources here.