Senior Applied Scientist, FinTelligence
Amazon - Bellevue, Washington, USA
Posted Apr 22, 2026
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
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- Mental health support
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- Salary
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- 401(k) match
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Schedule
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- Weekend work
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Application
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- Deadline
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Where they hire
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About this role
Senior Applied Scientist, FinTelligence Bellevue, Washington, USA At Amazon's FinTech organization, we are building AI systems that process hundreds of millions of financial transactions, turn complex documents into actionable intelligence, and power autonomous agents that learn from every customer interaction. We are looking for a Senior Applied Scientist to lead the development of generative AI applications that change how finance teams work, tackling problems at the intersection of large language models, multi-agent systems, and real-world financial operations. Key job responsibilities What You'll Work On - Building AI systems that finance teams trust enough to rely on without manual review, where precision isn't a nice-to-have, it's a compliance requirement - Designing agents that learn from user corrections and get measurably better with every interaction, not just at the next model release - Solving inference at massive scale using tiered model architectures, intelligent routing, and small language models that deliver production-grade accuracy at a fraction of frontier model cost - Developing evaluation frameworks that catch quality regressions before customers do and gate every model change before it ships Who Thrives Here - You're someone who cares as much about shipping as about research. - You've built models that run in production, not just in notebooks. - You're comfortable working across the full stack, from model architecture to deployment to measuring whether the customer's workflow actually changed. - You operate well in cross-functional settings where science, engineering, and business teams inform each other continuously. - You'd rather solve a hard real-world problem than
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