Staff Software Engineer, Continuous Learning

Aurora InnovationSan Francisco, CA
12hHybrid

About The Position

Aurora’s mission is to deliver the benefits of self-driving technology safely, quickly, and broadly. The Aurora Driver will create a new era in mobility and logistics, one that will bring a safer, more efficient, and more accessible future to everyone. At Aurora, you will tackle massively complex problems alongside other passionate, intelligent individuals, growing as an expert while expanding your knowledge. For the latest news from Aurora, visit aurora.tech or follow us on LinkedIn. Aurora hires talented people with diverse backgrounds who are ready to help build a transportation ecosystem that will make our roads safer, get crucial goods where they need to go, and make mobility more efficient and accessible for all. We’re searching for a Staff Software Engineer on the Autonomy Data: Continuous Learning team. The ideal candidate will have a passion for diving into our models and datasets. You will leverage state of the art foundation models as well as RLHF techniques to improve models with high quality data and build the datasets that power the Aurora Driver.

Requirements

  • BS in Computer Science, or a related field
  • Excellent Python, Proficient C++ programming and software design skills
  • Experience with storage and database management systems (e.g., one of SQL, no-SQL, protobuf, parquet, HDFS)
  • Experience working in a cloud environment (e.g., AWS, GCP, Azure, etc)
  • Knowledge and experience in at least one of computer vision, LLMs, or deep learning for other applications

Nice To Haves

  • Excellent C++ programming and software design skills
  • Distributed System design patterns (high availability, scaling, load balancing, caching, sharding etc.)
  • PyTorch and GPU programming experience

Responsibilities

  • Improve our dataset quality by establishing semi-automated evaluation mechanisms leveraging state of the art models as well as RLHF techniques
  • Expand our foundation model approach for sourcing interesting events to millions of miles
  • Own model training and inference pipelines for all core Autonomy models
  • Collaborate across teams and functions (product, program, operations, data science) to drive projects from inception to delivery
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