Sr. Applied Scientist, Applied AI Solutions
Amazon - Seattle, Washington, USA
Posted May 1, 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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- Relocation assistance
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- Childcare support
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- Learning budget
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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
Sr. Applied Scientist, Applied AI Solutions Seattle, Washington, USA The newest business group in AWS, Applied AI Solutions are built by AWS and AWS Partners to deliver applied AI solutions that leverage Amazon's operational expertise and that businesses love and trust for their day-to-day success. Our ambition is to become a partner which companies can rely on to run their business every day, putting AI to work delivering better customer experience, operational excellence and speed. Are you excited about pushing past single-turn chat into agents that run for hours or days, planning, recovering from failure, and accumulating knowledge as they work? Do you want to design the harnesses, memory systems, and context strategies that turn frontier models into reliablen collaborators on real business problems? Do you enjoy the prospect of solving agent problems that haven't been solved at scale anywhere before, including memory decay, belief revision, fact grounding, long-horizon credit assignment, and evaluation of open-ended trajectories? Along the way, you'll get opportunities to be a disruptor, innovator, and a problem solver, someone who truly enables AI agents to create significant impacts. Key job responsibilities You will partner with cross-functional business and engineering teams to identify and deliver high-impact agentic use cases across the Applied AI Solutions portfolio. You will design, develop, and evaluate long-running agents - including orchestration harnesses, memory architectures (episodic recall, semantic facts, revisable beliefs, principled decay), context compaction strategies, and safe, auditable tool and environment designs. You will define evaluation frameworks for agents whose outputs defy single-answer
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