Skip to main content

Zoox

Software Engineer - C++ Core Data

Foster City, CA$175k–$250kfull-timemidAdded today

About this role

Lead the design and optimization of a large-scale data management system for autonomous vehicles, working with C++ to handle millions of messages per minute from on-vehicle sensors while coordinating across multiple engineering teams to ensure data reliability and regulatory compliance.

What you'll do

  • Develop and optimize multi-threaded C++ code for efficient data processing on autonomous vehicles
  • Design scalable software frameworks to identify and prioritize critical driving data in real-time
  • Manage petabyte-scale data pipelines from collection through analysis and storage
  • Collaborate with on-vehicle software teams to integrate new data frameworks and drive adoption
  • Partner with autonomy, hardware, safety, and legal teams to ensure system compliance and dependability
  • Minimize robot turnaround time while maintaining access to essential business data

What they're looking for

  • C++ (multi-threaded programming)
  • Large-scale data systems design
  • Performance optimization and profiling
  • Data pipeline architecture
  • Distributed systems
  • Cross-functional collaboration
  • Real-time systems programming
  • Regulatory compliance knowledge
Apply with Autofill

Opens the application — the Jobs AI extension fills it for you. Set up autofill

Opens the official application on the employer’s site. No login required.

Zoox

Zoox develops autonomous vehicle technology and robotaxi systems, supported by manufacturing operations and AI validation infrastructure. The company is hiring for part-time student roles in hardware-software integration, manufacturing software engineering, AI testing and evaluation, and QA automation, as well as experienced engineers for simulation and AI performance assessment.

Website
zoox.com
View all jobs at Zoox

Likely interview questions

  • Describe your experience optimizing multi-threaded C++ applications for high-throughput, low-latency systems.
  • How have you approached identifying which data is most critical to capture in a resource-constrained environment?