In this role, you will:
- 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.
Qualifications
- 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
Bonus Qualifications
- Experience building and/or evaluating ML models that operate on raw sensor data (detection, classification, tracking), including ethical AI validation
- Experience incorporating ML validation into MLOps frameworks and automation
- Experience with C++, JavaScript
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