Senior Software Engineer, Labeling Tools (C++)

NuroMountain View, CA
48d$193,930 - $291,150

About The Position

Nuro takes a machine-learning-first approach to autonomous driving technology. In an ML-first system, the overall system performance depends heavily on the quantity and diversity of its training and evaluation data. The team plays a crucial role in the advancement of autonomous driving systems by ensuring teams have access to high quality labeled data. This is facilitated by a comprehensive labeling stack featuring an expressive workflow definition framework, scalable infrastructure, and a suite of data annotation tools. Nuro works with industry leading sensors. Our tools must handle efficient visualization and annotation of millions of points of sensor data. Improving label quality is an iterative and experimental process in collaboration with Nuro's autonomy teams. The team partners closely with autonomy engineers to ensure teams have high quality labeled data in a timely manner.

Requirements

  • You have a degree in B.Sc or M.Sc. plus 5 years of relevant work experience
  • Must have experience in application development using C++
  • You have excellent communication, presentation, interpersonal, and analytical skills
  • You have experience setting team or project product and technical vision, timelines and prioritization
  • You are committed to improving the code quality, setting technical standards and best practices across the broader software organization

Nice To Haves

  • Knowledge of data engineering, its tooling and best practices
  • Knowledge of graphics engineering, CPU and gpu optimization
  • Experience working with sensor data and data visualization
  • Experience with profiling and performance tuning native applications
  • Experience working with large-scale distributed data systems

Responsibilities

  • Define the vision and build an industry-leading suite of autonomy data labeling tools
  • Productionize robust tooling for our state-of-the-art sensor suite
  • Own the release and feedback loops for a dynamic, high-volume labeling workforce
  • Explore novel user workflows to drive label quality improvement
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