Applied Scientist, Customer

Applied Scientist

What happens when you give AI the ability to remember? Not cached responses — real structured memory that compounds over time and transfers across contexts. We're building the science behind this, and we need researchers who want to own the problem end-to-end. This is a founding role on a new team. You won't inherit models or maintain someone else's pipeline. You'll define the research direction, run experiments at scale, and ship what works directly to production.

Key job responsibilities:

• Design and implement novel approaches to knowledge extraction from heterogeneous, unstructured data sources at organizational scale.
• Build retrieval systems that match intent to relevant knowledge across domains — solving the "right memory at the right time" problem.
• Own the quality of memory generation: what to capture, how to structure it, when to surface it, and when to let it decay.
• Run large-scale experiments using Amazon's compute infrastructure and massive real-world datasets.
• Develop evaluation frameworks for a system where "quality" means something new — right knowledge, right context, right confidence level.
• Collaborate with engineers to move from research prototype to production system in weeks, not quarters.
• Invent new approaches to temporal knowledge management — how memories age, conflict, and compound over time.
• Publish and patent novel approaches to knowledge acquisition and retrieval at top-tier venues.

A day in the life:

You will solve real-world problems by getting and analyzing large amounts of data, generate insights and opportunities, execute experiments, and develop statistical and ML models. The team is driven by business needs, which requires collaboration with other Scientists, Engineers, and Product Managers across the organization. You get to influence stakeholders with clear communication skills. You innovate on behalf of the customer and strategically build features.

You will mentor junior members and help them grow.

About the team:

We're a new team within Personalization, focused on a different kind of recommendation: not "what product should this customer see" but "what knowledge should this AI use right now." Same scale, same rigor, entirely new problem space. The science is at the intersection of information retrieval, knowledge representation, and LLM reasoning — and the right approach hasn't been established yet. The team values innovation and offers a safe place to try, fail, and learn while fostering a culture of continuous improvement.

Everyone is a leader and owner for everything we do as a team. We offer creative space with an entrepreneurial work environment focusing on customer obsession.