Senior Software Engineer, Nuro

• You have a degree in B.Sc or M.Sc. plus 4 years of relevant work experience
• Strong proficiency in Python, Golang, or similar languages
• Domain experience: Experience working with large scale data and building scalable & reliable systems / data pipelines; ability to understand and design complex systems
• Engineering leadership: Experience setting team or project product and technical vision, timelines and prioritization; formally or informally being a Tech Lead, mentoring and support junior engineers
• Technical excellence: Ability and willingness to deep dive into implementation, driving technical standards and best practices across broader software organization
• A bachelor's degree in Computer Science, Electrical Engineering, or a closely related field

Desirable

• 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
• Our team aims to evolve our Evaluation ecosystem to become more comprehensive and easy to use by Autonomy Behavior, Verification, and Validation teams
• Accelerate Autonomy Iteration by Strengthen Evaluation Signals
• Promote Evaluation Consistency across Behavior and Systems
• The team plays a crucial role in the advancement of autonomous driving systems by creating a scalable and reliable evaluation platform and visualization tooling
• This infrastructure is designed to produce training and evaluation data derived from both on-road collected logs and simulation logs
• Additionally, the team collaborates closely with system engineers to thoroughly validate the autonomous driving system before its deployment
• About the Work
• Design and develop unified, introspectable, scalable batch, and streaming data pipelines that can ingest and process data across a wide range of use cases relevant to evaluation
• Create and implement a storage system capable of accommodating both the large volume and diverse range of evaluation and performance metrics
• Construct intuitive dashboards and reports to present evaluation results, facilitating straightforward comparisons that highlight both improvements and regressions of the ML components and the overall system
• Develop and maintain continuous testing and monitoring systems to guarantee the integrity and resilience of our data and associated data pipelines
• Create visualization tools to aggregate, modify, and introspect evaluation results and metadata