- Set the long-term scientific vision and team strategy: Define what best-in-class agent performance evaluation and learning look like across Agentic WorkSpaces - for computer-using agents and human-AI teams alike. Chart a multi-year research roadmap and build the team and plan to deliver it. Secure buy-in from VP-level leadership.
- Hire and grow scientific talent: Own recruiting calibration development and retention for the science team. Mentor scientists toward senior and principal scope and raise the scientific bar across the organization.
- Direct research on highly ambiguous novel problems: Guide the team through foundational challenges in agent perception reasoning evaluation reliability and human-AI collaboration - problems where neither the approach nor the success criteria are pre-defined.
- Drive cross-organizational alignment: Work across partner teams (AgentCore Bedrock model teams Identity Security the MCP ecosystem) and across the Applied AI Solutions product portfolio with product and engineering leadership to ensure scientific decisions compose into a coherent product.
- Deliver measurable business impact: Ensure your team's research translates to customer outcomes: higher task accuracy lower cost-per-action faster time-to-production measurable productivity for human-AI teams and the trust that lets enterprises scale agent workflows.
- Establish scientific rigor and operational excellence: Set the standard for experimentation evaluation and reproducibility and the mechanisms that keep the science organization productive and accountable.
- Advance the state of the art: Enable and champion contributions to the external technical community through publications patents and open-source work that position AWS as the leader in the science of secure agent-computer interaction and human-AI teamwork.
- Translate science into shipped product. Partner with engineering, product, and program leaders to move models evaluation and learning systems from prototype into a decade-old production service operating at massive scale without compromising the reliability that customers depend on.
- Represent science in leadership and to customers. Be the scientific voice in org-level planning and roadmap decisions across AAWS and engage directly with enterprise customers on how agent performance safety and human-AI productivity are measured and earned.
-Master's degree in Computer Science, Machine Learning, AI, Statistics, or equivalent
-Experience building large-scale machine learning and AI solutions at Internet scale
-Experience distilling informal customer requirements into problem definitions, dealing with ambiguity and competing objectives
-Experience hiring and leading experienced scientists as well as having a successful record of developing junior members from academia or industry to a successful career track
-10+ years of practical work applying ML to solve complex problems for large-scale applications experience
-5+ years of hands-on work in big data, machine learning and predictive modeling experience
-5+ years of people management experience
-PhD in Computer Science (Machine Learning, AI, Statistics, or equivalent)
-Experience in practical work applying ML to solve complex problems for large scale applications
-Experience working with big data, machine learning and predictive modeling
-Experience in people management
-Experience with big data technologies such as AWS, Hadoop, Spark, Pig, Hive etc.
-Experience with Java, C++, or other programming language, as well as with R, MATLAB, Python, or an equivalent scripting language
-Experience researching actual applications
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