Staff Software Engineer, Continuous Learning

Aurora Innovation
San Francisco, California$189k – $303kPosted 27 March 2026

Job Description

Who we are 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. 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. #LI-KW1 #Mid-Senior Working at Aurora At Aurora, we bring together extraordinarily talented and experienced people united by the strength of our values. We operate with integrity, set outrageous goals, and build a culture where we win together — all without any jerks. We believe in-person work increases collaboration, empathy and our ability to lead effectively. As a result, we operate in a hybrid work environment where Aurorans are in office at least 3 days per week. Our Careers page provides insight into what it is like to work at Aurora, and you can find all the latest updates in our Newsroom . Our commitment to safety At the core of everything we do is our commitment to safety. Building best-in-class self-driving technology will take time, and we believe that each employee at Aurora has a role in contributing to safety, every step of the way. Aurora expects commitment to our safety policies from every employee, and seeks candidates who take an active responsibility, can contribute to building an atmosphere of trust, and invest in the organization’s long-term success by prioritizing working safely, no matter what. Our commitment to inclusion Aurora considers candidates without regard to their race, color, religion, national origin, age, sex, gende ... (truncated, view full listing at source)
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