Software Engineer, ML Infrastructure, Optimization

About the role

Join our team to build the infrastructure that powers autonomous driving software. You will focus on optimizing machine learning models and pipelines to ensure high performance for our licensing technology. In this capacity, you own the design and implementation of systems that extract maximum efficiency from our machine learning workflows. You are responsible for collaborating closely with domain experts to refine the Nuro Driver and Nuro Toolkit software stacks in a production environment. Your work will directly support the scaling of infrastructure required for the transition toward robotaxi and personally owned autonomous vehicles. You will monitor and iteratively improve performance metrics for large-scale autonomous driving datasets to guide product decisions. This role is embedded within the ML Infrastructure team, where your contributions will be visible across the entire autonomy stack.

Key facts

What you'll do

• Design and implement systems to improve the efficiency of machine learning model training and inference across distributed compute clusters.
• Collaborate with engineers to refine the Nuro Driver and Nuro Toolkit software stacks, ensuring alignment between infrastructure capabilities and application requirements.
• Scale infrastructure to support the transition toward robotaxi and personally owned autonomous vehicles while maintaining strict performance and reliability standards.
• Monitor and improve performance metrics for large-scale autonomous driving datasets, identifying bottlenecks and driving optimization initiatives.
• Implement robust data processing pipelines that handle the complexity and volume of sensor data required for training and validation.
• Partner with machine learning researchers to optimize model architectures for deployment in latency-constrained automotive environments.
• Develop tooling and frameworks that enable other engineers to diagnose and resolve performance issues quickly and effectively.
• Contribute to the creation of best practices for model optimization and infrastructure management within the ML Infrastructure team.
• Work closely with cross-functional stakeholders to translate high-level product goals into technical requirements for infrastructure development.
• Ensure that all solutions are maintainable, scalable, and aligned with the long-term vision for Nuro's licensing technology platform.

Requirements

• Proficiency in software engineering with a focus on machine learning infrastructure, including experience with distributed systems and data pipelines.
• Experience with performance optimization of deep learning models, including techniques such as quantization, pruning, and kernel optimization.
• Ability to work within the Mountain View headquarters, demonstrating consistent presence and collaboration with on-site teams.
• Strong understanding of software development lifecycle practices, including version control, code review, and continuous integration.
• Familiarity with cloud computing platforms and containerization technologies used to deploy machine learning workloads at scale.
• Demonstrated ability to debug complex systems and analyze performance metrics to drive iterative improvements.
• Excellent written and verbal communication skills, with the capacity to explain technical concepts to both technical and non-technical audiences.
• Willingness to engage in hands-on coding and system design tasks that directly impact the performance and reliability of production infrastructure.

Skills & tools

• Machine Learning Infrastructure
• Model Optimization
• Autonomous Driving Software
• Nuro Driver
• Nuro Toolkit

Practical notes

• Nuro recently secured 106 million dollars in funding to support the shift toward licensing autonomous driving technology.
• The company is currently expanding its focus from delivery robotics to robotaxis and personally owned autonomous vehicles.
• This role is based in Mountain View, California, and requires physical presence at the headquarters office.
• Full-time engagement is expected, with hours aligned to standard business operations and occasional cross-team coordination needs.
• No specific visa sponsorship details are provided in this listing; candidates must ensure eligibility to work in the designated location.
• Applications should be submitted within the timeframe managed by the hiring team, as roles may close once filled or upon internal review cycles.
• Compensation details are not included in this public job description and are discussed during the interview process with authorized representatives.