Senior Machine Learning Validation Data Scientist, Zoox

• Bachelor's, Master's or PhD in CS, statistics, mathematics or relevant fields, including statistics (bayesian, parametric statistics, non-parametric, experiment design) and their python-based frameworks (numpy, scipy)
• Experience designing and building evaluation systems for classical and deep-learning models, including linking standard model metrics to end-to-end system behavior and key business metrics
• Experience building and presenting summary metrics to key stakeholders, including advanced knowledge of data visualization techniques
• Experience handling big data, including exposure to distributed data and compute (e.g. Hadoop, Spark, Hive, MapReduce, Databricks), and traditional data query & manipulation (e.g. SQL, NoSQL, Databases)
• Experience adhering to software best practices, e.g. unit testing, DRY, abstraction, modularity, clean interfaces

Desirable

• You will be part of a team building state-of-the-art Artificial Intelligence (AI) solutions for the Zoox autonomous driving Perception stack. As a member of the Perception team, you will be part of the “eyes and ears” of the Zoox autonomous driving stack, deploying state-of-the-art AI solutions to ensure safe and effective driving
• In this role you will be responsible for designing and building systems to validate models on large (PBs) multimodal data
• Your work will have a broad impact on shipping high performance ML models by ensuring we have comprehensive and robust evaluation pipelines to ensure safe and effective driving behavior. Think you have what it takes to build the best machine learning systems on this planet? Come join us!
• Develop and evangelize our strategy for data development and model evaluation metrics to ensure safe and effective autonomous driving, including defining objective success criteria for ML model deployment
• Analyze and interpret validation outcomes to provide recommendations to improve the performance of the ML models
• Support data-centric AI by: exploring how data impacts ML model performance; guide data labeling strategies; build frameworks for measuring and improving on label quality
• Define key performance metrics that will help Perception engineers to directly measure the impact of the new features they are developing on the end-to-end driving behavior
• Stay up-to-date with the latest developments in machine learning validation techniques and best practices for perception in autonomous vehicles