Staff Software Engineer, Continuous Learning

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.

In this role, you will

• 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

Required Qualifications

• 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

Desirable Qualifications

• Excellent C++ programming and software design skills


• Distributed System design patterns (high availability, scaling, load balancing, caching, sharding etc.)


• PyTorch and GPU programming experience

The base salary range for this position is $189,000-$303,000 per year. Aurora’s pay ranges are determined by role, level, and location. Within the range, the successful candidate’s starting base pay will be determined based on factors including job-related skills, experience, qualifications, relevant education or training, and market conditions. These ranges may be modified in the future. The successful candidate will also be eligible for an annual bonus, equity compensation, and benefits.

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#Mid-Senior