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Home›Jobs›Zoox›Senior Full Stack Software Engineer - ML Ops Tooling Platform
Zoox

About Zoox

Revolutionizing transportation with autonomous vehicle technology

🏢 Tech, Automotive👥 1001+ employees📅 Founded 2014📍 Vintage Park, Foster City, CA💰 $1.2b⭐ 3.9
B2CArtificial IntelligenceCarsTransportElectric VehiclesAutomation

Key Highlights

  • Raised $1.2 billion in Series B funding
  • Headquartered in Foster City, CA with 1001+ employees
  • Innovative vehicle design with independent wheels and no front/back
  • Part of a $2 trillion industry projected by 2030

Zoox, headquartered in Foster City, CA, is pioneering autonomous vehicle technology with a unique driverless ecosystem. The company has raised $1.2 billion in Series B funding and employs over 1,000 people. Its innovative vehicle design, revealed in December 2020, features independent wheels and a b...

🎁 Benefits

Zoox offers work-from-home opportunities, comprehensive health insurance, and generous paid parental leave. Employees enjoy unlimited flexible paid ti...

🌟 Culture

At Zoox, the culture is centered around innovation in autonomous transportation, with a focus on safety and enjoyment. The company promotes a collabor...

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Zoox

Senior Full Stack Software Engineer - ML Ops Tooling Platform

Zoox • Boston, MA

Posted 2 years ago🏛️ On-SiteLeadSoftware engineering📍 Foster city💰 USD250,000 - USD300,000 / year
Apply Now →

Job Description

This team owns the data and data platform to support all Perception model training and validation, as well as system validation beyond Perception. The platform supports sample curation, data mining, data labeling, and ML dataset management. High-quality data and efficient data flywheel are central to ML model development, enabling teams to efficiently iterate and improve model performance at scale while Zoox expands its business domain.

In this role, you will improve and scale the platform, while also building new features and expanding cross-pillar adoption through Python/C++ tooling, APIs, and data engineering. Your work spans backend API development, data pipelines, and integration with model training and testing workflows, ultimately providing the infrastructure that powers data flywheel at scale.

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