Назад
1 день назад

Software Engineer (Mapping)

Формат работы
hybrid
Тип работы
fulltime
Грейд
middle
Английский
b2
Страна
US
Вакансия из списка Hirify.GlobalВакансия из Hirify Global, списка международных tech-компаний
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Мэтч & Сопровод

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Описание вакансии

Текст:
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TL;DR

Software Engineer (C++/Computational Geometry): Building geometry algorithms and data interfaces for road networks with an accent on network topology, data format, and validation. Focus on developing scalable post-processing pipelines and high-performance APIs for on-vehicle real-time access.

Location: Hybrid in Foster City, CA

Company

Zoox is developing autonomous mobility solutions to enable safe and reliable autonomous driving for urban transportation.

What you will do

  • Develop and maintain geometry algorithms that power map data interfaces.
  • Implement mapping data formats, validation frameworks, and post-processing pipelines.
  • Collaborate with cross-functional teams to ship and expand APIs for downstream consumers.
  • Create design documentation, test plans, and supporting artifacts for shipped features.
  • Contribute to all software development phases, from prototyping and requirements to implementation and validation.

Requirements

  • Bachelor's degree or higher in Computer Science or a related field.
  • 3+ years of relevant industry experience.
  • Proficiency in C++.
  • Strong mathematical skills, particularly in computational geometry.
  • Experience working with large datasets and strong code-quality fundamentals (correctness, testability, modularity).
  • Must be based in or able to work hybrid in Foster City, CA.

Nice to have

  • Experience with geographic data, spatial acceleration structures, or surface meshes.
  • Familiarity with road network topology.
  • Knowledge of statistics and metric design.
  • Experience with software profiling or performance tuning.

Culture & Benefits

  • Inclusive environment where diverse perspectives are valued to foster innovation.
  • Opportunity to work on foundational technology critical to scaling autonomous driving.

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