Senior Systems Engineer - Perception

In this role, you will:

• Build large-scale validation and metrics systems using real world structured testing, state-of-the-art simulations, distributed computing, and modern data analysis technologies
• Bridge the gap between systems engineering and state-of-the-art machine learning approaches by collaborating with partner teams, designing robust evaluation system architecture, building up scalable metrics/tools, and leading the verification and validation efforts at various levels of abstraction
• 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.
• 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 Robotics, electrical engineering, systems engineering or related fields
• Domain experience working on autonomous vehicles, preferably on the autonomy software stack, such as the perception, prediction, or planner modules
• Experience with developing functional requirements driven software components
• Strong understanding of statistical analysis, experimental design, and hypothesis testing, including parametrics & non-parametric testing
• Fluent with Python-based data analysis and visualization, including use of libraries such as numpy, scipy, pandas, statsmodels, matplotlib, seaborn, plotly, and pyspark, as well as experience with data query languages and common databases, e.g. SQL, NoSQL, Databases

Bonus Qualifications

• Experience with automation functional safety standard such as ISO 21448, ISO26262, ASIL, DO-178B/C, etc
• Have performed hazard analysis using one or more of the following methods: System Theoretic Process Analysis (STPA), qualitative or quantitative Fault Tree Analysis (FTA), Failure Modes and Effects Analysis (FMEA), Hazard and Operability Analysis (HAZOP), Functional Hazard Analysis (FHA), etc
• Experience building or evaluating classical and deep-learning models that operate on raw sensor data (detection, classification, tracking, etc.), including linking standard model metrics to end-to-end system behavior and key business metrics.