Full Job Description
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 judgment, building trajectory-level evaluations, reward-hacking detection, and human-in-the-loop review patterns. As a senior scientist, you will set technical direction, mentor scientists and engineers, and represent the science org in roadmap and architecture decisions.
You will ensure seamless deployment and integration of agents into production systems customers rely on daily.
The ideal candidate thrives in ambiguity, builds collaborative relationships, moves fast, and is passionate about customer experience. They hold a strong point of view on where agent capabilities are heading, with the scientific rigor to separate what works from what merely demos well. They lead by example, communicate effectively across audiences, and love solving complex problems at the frontier of applied AI.
BASIC QUALIFICATIONS
- 3+ years of building machine learning models for business application experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning
- 5+ years of applied research experience
- PhD in CS (Computer Science), CE (Computer Engineering), or related technical field, OR MSc + 10 years, OR BSc + 12 years
PREFERRED QUALIFICATIONS
- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, WA, Seattle - 167,100.00 - 226,100.00 USD annually
