Software Engineer (Planner Frameworks Pipeline)

• We are a small, scrappy team within the larger Autonomy organization. Although this role primarily involves off-vehicle pipelines, you will work closely with engineers developing the on-vehicle algorithms and models in our autonomy stack. We stay close to the end users - autonomy engineers - and think about the end to end use case for these validation pipelines
• This is a hands-on role with a high degree of independence and ownership. You will be expected to contribute towards the framework’s architecture, reliability, efficiency, and grow its capabilities to support new use cases
• Improve the cost efficiency, reliability, and performance of our validation and simulation pipelines
• Create production-grade APIs, SDKs, and tools to enable a varied set of validations of autonomous behaviors
• Improve the ML training pipelines supporting the autonomous behavior org

Benefits

• Paid parental leave
• Affinity groups and sports clubs
• Work from home opportunities
• Health insurance
• Our crew’s health and happiness is our first priority. We offer comprehensive health and mental health support, a wellbeing program, and unlimited and flexible paid time away
• We invest in our crew—and their families—for the long term. That includes generous family planning support, caregiver support, and strong cash compensation with great equity upside
• We look after our crew when they’re in the office too. Our famous food program is a great example, featuring a daily changing menu of local and sustainable dishes
• There’s a busy calendar of social events at Zoox, with more sports teams than you can count. And, of course, playing with robots is an important part of the job description
• You should have a track record of keeping production systems running with high availability. Experience with robotics or autonomous systems is not required but an understanding of the robotic data lifecycle is preferred
• Proficiency with Python and familiarity with C++
• Experience with AWS or similar providers
• Bachelor’s degree in Computer Science or related field and 6+ years of industry experience
• Experience optimizing large-scale distributed systems for cost and efficiency
• Exposure to machine learning workloads (training, inference, data generation) from a cost optimization perspective
• Background in algorithmic optimization or performance investigation
• Experience with Ray.io, particularly Ray Core and Ray Data