Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

The role involves developing automotive body assembly processes, leading factory layouts, collaborating with engineering teams, and maintaining process documentation.

The Strategic Sourcing Manager is responsible for direct procurement and partnerships, leading cost reduction efforts, supplier evaluations, negotiating contracts, and managing third-party relationships.

As a Senior Software Engineer - C++ GPU Performance, you will optimize GPU algorithms, monitor performance metrics, and influence design decisions for self-driving technology.

Lead design and architecture of SAP Convergent Mediation frameworks; configure mediation pipelines, transformations, enrichment, and routing; design data flows with upstream sources, CC, and downstream BRIM; optimize performance and fault tolerance; implement monitoring, alerting, and reconciliation; provide technical leadership and collaborate with finance, product, engineering, and vendors.

The role involves leading signal integrity and EMC strategies for autonomous vehicles, ensuring electrical reliability and compliance through simulations, testing, and collaboration across teams.

Lead and grow a team building large-scale reinforcement learning and ML behavior models for autonomous driving. Set technical strategy, coordinate with partner teams, oversee training and efficient inference, monitor KPIs, and manage team resources.

The Senior Technical Program Manager will oversee complex manufacturing infrastructure projects, manage external partners, and ensure cross-functional integration to achieve seamless operational readiness in manufacturing.

The System Safety Engineer will analyze complex electronic and software systems for safety, lead safety analysis projects, and collaborate to improve compute platform designs in autonomous vehicles.

Lead cross-functional initiatives to ensure safety requirements for Zoox's autonomous software and vehicle platform. Oversee verification and validation programs, manage risks, and drive program execution across teams.

Lead data-quality and labeling efforts for perception ML: implement scalable quality measures, drive data-centric model improvements, maintain ontology/class structure, evaluate human vs auto-labeling trade-offs, and build metrics for labeling throughput and annotator/vendor quality.