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As a Staff ML Accelerator Architect at Waymo, you will analyze workloads, architect machine learning solutions for autonomous driving, simulate high-performance architectures, and collaborate with engineering teams to optimize hardware and software.

The Technical Lead for Perception Fallback Systems will design and lead the architecture for alternate perception pipelines in autonomous driving. Responsibilities include developing machine learning algorithms, collaborating with various teams, and optimizing perception stacks for degraded conditions. The role also involves technical leadership within the perception domain and engagement with hardware and software engineering teams.

As a Summer Intern at Waymo, you will design advanced perception models, code in Python and C++, and train/evaluate models on large datasets. You will collaborate with industry leaders on impactful projects, gaining valuable experience in autonomous driving technology.

As a Senior Software Engineer at Waymo, you will design software features, improve the realism of simulations, and ensure product scalability and reliability. You will also mentor team members and build user-facing products while focusing on back-end development and architecture.

As a Staff Software Engineer for Infrastructure at Waymo, you will design and develop storage infrastructure features, manage datasets, and enhance the reliability of production systems. You will collaborate with partner teams and ensure efficient storage management while improving code quality and automation. Your role demands extensive experience in software engineering and distributed systems, especially in large-scale storage systems.

As a Staff Software Engineer, you will design architecture and software features for Waymo's simulation product, improve driving realism, ensure scalability and reliability, and mentor senior engineers. You will work on backend systems and collaborate with front-end teams to meet the needs of internal customers.

As a summer intern at Waymo, you will work on building infrastructures for custom kernels integrations for Transformer-based models, evaluate quantization techniques and custom kernels, and optimize end-product models, while learning from leaders in the industry.