Top Tech Jobs & Startup Jobs

Lead and manage a team focused on embedded software testing and simulation, develop simulation frameworks, and ensure infrastructure reliability.

Develop and optimize emulation testbenches and infrastructure to validate silicon and software, write synthesizable transactors and C-DPI interfaces, triage hardware/software failures, assist post-silicon bring-up, and build tools for automation, CI, regression, and user support.

Define and lead microarchitecture and RTL implementation for next-generation automotive SoCs. Drive IP/subsystem design (caches, NoC, accelerators), optimize power/performance/area, collaborate with firmware/software teams, mentor engineers, and manage vendor IP selection through tapeout.

Design, build, and deploy production-grade agentic AI systems and scalable evaluation frameworks. Develop automated metrics to measure accuracy, safety, and reliability of non-deterministic agents. Automate and scale evaluation infrastructure to improve characterization and assessment of onboard autonomous driving software.

Develop high-quality C++ software systems for managing autonomous vehicle functions, analyze data, and participate in cross-functional workflows.

You will create large datasets and training recipes, develop methods for data mining, create scalable infrastructure solutions, and collaborate with ML infrastructure teams.

As a Staff Technical Lead Manager, you'll enhance driving behavior technologies for Waymo's autonomous vehicles, collaborating across teams to implement data-driven solutions and improving performance and safety.

Lead the technical architecture for the in-car experience, manage cross-team bottlenecks, and mentor engineers while contributing to design and code.

Own and develop concurrent C++ backend services for Webviz to stream time-series and sensor data, integrate offboard storage and WebRTC, optimize latency and throughput, build APIs for automated triage/evaluation, plan technical roadmaps, and mentor engineers.

Build and maintain Waymo's scalable, high-throughput, low-latency inference platform. Host and integrate internal and external ML models (including LLMs), optimize inference efficiency to increase throughput and reduce cost, and deploy model inference solutions for distillation, evaluation, dataset generation, active learning, and auto-labeling.