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

Design, build, and maintain scalable data pipelines, ETL/ELT processes, data lake and warehouse infrastructure, and cloud-based data platforms. Optimize systems for performance, reliability, security, and cost while implementing monitoring, governance, and data quality practices. Collaborate cross-functionally with data scientists, analysts, engineering, and product teams to deliver reliable data solutions and improve the data ecosystem roadmap.

Design, develop, and maintain scalable backend infrastructure and data-processing pipelines for Aurora’s static and dynamic mapping systems. Build reliable services for storing and serving map data, improve testing and performance, and collaborate with internal and external customers on system designs and features. The role focuses on distributed systems, data versioning, cloud-native technologies, and high-throughput mapping infrastructure supporting autonomous vehicles.

Lead the strategy, architecture, configuration, integration, governance, and AI automation of Aurora’s HR technology ecosystem. Oversee HRIS, ATS, rewards, and performance platforms; improve data flows across People, Finance, and Commercial systems; establish privacy, compliance, and audit controls; partner with executives and vendors; and build and mentor a high-performing People Technology team.

Lead cybersecurity product strategy and large-scale security programs across Aurora’s software, hardware, and services. Partner with product, engineering, suppliers, and development partners to define security features, manage risks, establish roadmaps, integrate security assurance and governance into product development, and track security KPIs. The role requires influencing cross-functional teams, managing requirements and dependencies, and ensuring products meet cybersecurity standards, particularly in automotive and autonomous vehicle environments.

Optimize deep learning networks for autonomous vehicles and large-scale data centers. Analyze and troubleshoot performance using profiling and roofline modeling, improve software architecture, latency, GPU memory usage, and throughput, and deploy scalable models with frameworks such as PyTorch or TensorFlow. Collaborate across engineering and research teams on large codebases, applying CUDA, C++, Python, and inference acceleration technologies in Linux/Unix environments.

Research and explore Vision-Language-Action and Vision-Language Model architectures for autonomous driving. Identify model limitations, redesign architectures, translate academic research into scalable production code, optimize models for supercomputing environments, and mentor engineering teams transitioning into this area.

Develop real-time behavior-planning algorithms for autonomous vehicles, selecting safe and comfortable trajectories. Integrate motion planning with machine learning, build production-grade Aurora Driver software, and create metrics and testing workflows for algorithm iteration. The role requires strong Python or C++ programming experience in low-latency or real-time environments and experience productionizing motion-planning algorithms. GPU and machine-learning toolkit expertise is preferred.