Machine Learning Engineer - Perception

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

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Seniority

Mid From the posting source

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About this role

Machine Learning Engineer - Perception Foster City, CA The Perception team at Zoox is at the forefront of leveraging GenAI to create synthetic data, unlocking scalable training and evaluation for our autonomous system's perception and entire stack. As a Generative AI Engineer, you will develop and train cutting-edge models for sensor-level scenario generation, utilizing world models and radiance fields techniques with large-scale proprietary data. This role directly impacts the productivity, safety, and capabilities of Zoox's autonomous system by validating algorithms in real-world conditions. The Perception team at Zoox is at the forefront of leveraging GenAI to create synthetic data, unlocking scalable training and evaluation for our autonomous system's perception and entire stack. As a Generative AI Engineer, you will develop and train cutting-edge models for sensor-level scenario generation, utilizing world models and radiance fields techniques with large-scale proprietary data. This role directly impacts the productivity, safety, and capabilities of Zoox's autonomous system by validating algorithms in real-world conditions. In this role, you will: - Design, develop, train and evaluate multi-sensor fusion based deep learning models to understand obstacles and environmental context - Understand and curate real and synthetic datasets to improve our models - Perform latency optimization and deploy models to our robot fleet - Build a deep understanding of Perception gaps and behavioral issues around difficult obstacle types in order to help plan and prioritize our work - Collaborate with Prediction/Planner team to deploy fully autonomous vehicles in environments with difficult and rare obstacles, extreme weather conditions, and complex

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