Evaluate and enhance safety-critical AI systems by architecting statistical methodologies and managing validation pipelines while driving data-driven insights across teams.
Design Evaluation Frameworks: Architect statistical methodologies for safety-critical AI systems to form objective, rigorous conclusions about their performance and reliability.
Conduct Robust Analysis: Deliver validation evidence to support increasingly complex operations and identify potential edge-case failures.
Inform Strategy: Deliver clear, data-driven insights to development teams to guide system improvement, and to executive leadership to inform milestone-level go/no-go decisions.
Define Metrics: Drive alignment across engineering teams on performance metrics and data extraction strategies.
Lead the Lifecycle: Manage all phases of evaluation including prototyping, requirements capture, design, implementation, and validation.
Scale Pipelines: Partner with engineers to build and maintain scalable data processing and simulation pipelines, applying distributed computing to analyze petabytes of driving data.
Hands-on experience with production machine learning pipelines: dataset creation, training frameworks, metrics pipelines
Experience with modern data processing technologies such as Apache Spark, Spark SQL, and Databricks
Experience with designing metrics and delivering actionable insights that drive business decisions
Zoox is building a fully autonomous vehicle fleet from the ground up, coupled with the ecosystem necessary to launch this technology into urban environments. By integrating robotics, machine learning, and innovative design, Zoox is paving the way for a new era of mobility-as-a-service.
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