Senior Software Engineer, Linux Kernel

Waymo Platforms is responsible for low-level software and infrastructure on various components of the Waymo self-driving system. The team's scope covers everything from low-level system software to high-bandwidth data logging, IPC (low-latency and high-throughput), networking, scheduling, process management, performance analysis, full-system debugging, etc. We work closely with the Hardware, Compute, Sensor, Perception, Behavior and Machine Learning teams to build the most performant and low-latency self-driving solution in the industry.

In this role, you will work on the Core Platform Team. The Core Platform team owns the primary compute node of Waymo's on-vehicle compute platform – a highly integrated, high performance embedded system.If you enjoy building robust and high-performance embedded systems using state-of-the-art compute hardware and working closely with engineering teams that will be direct consumers of your work, you'll be a great fit for this role.

In this role you'll:

• Work on a small team of System Software and Linux Kernel Engineers to design, implement and test system software components from early prototyping to production launch of driverless vehicles
• Design, implement and test Linux Kernel device drivers and other components for high-performance embedded systems including custom ASICs and accelerators.
• Take on complex and ambiguous problems and come up with clean and robust solutions.

At a minimum, we'd like you to have:

• BA/BS degree in Computer Science, Computer Engineering or relevant technical field or equivalent practical experience
• 8+ years of relevant work experience
• Proficiency in C and C++
• Strong understanding of programming in an asynchronous environment with multiple processes, threads, timers, interrupts, etc.
• Experience with Linux kernel and device driver development
• Excellent debugging skills

It’s preferred if you have:

• Experience with common hardware protocols/interfaces (PCIe, Ethernet, I2C, etc)
• Working knowledge of complex hardware-accelerated compute pipelines (GPUs, ML accelerators, image/data processors, etc.)
• Performance optimization of systems and complex pipelines
• Working knowledge of hardware bring-up and debugging

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