Software Engineer II, ML Infrastructure

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. Aurora's MLOps team is dedicated to building a cutting-edge platform that accelerates the development and evaluation of machine learning models for autonomous vehicles. This is a unique opportunity to tackle challenges that range from optimizing distributed training code to designing intuitive UIs for ML engineers. We’re searching for a Software Engineer II on our ML Infrastructure Team.In this role, you will• Architect and develop Aurora’s ML platform for launching, monitoring, and comparing ML model training pipelines.• Work closely with machine learning engineers in the Autonomy groups to understand and address their platform and tooling needs, ensuring rapid and efficient model training.• Design and implement user interfaces that are integrated with distributed systems, emphasizing availability, scalability, and operational excellence.Required Qualifications• Bachelor’s or Master’s degree in Computer Science or a related field.• 2+ years of relevant experience.• Proficiency in Python or Go.• Hands-on experience with cloud environments like AWS or GCP.• Understanding of machine learning training workflows.• Experience building distributed systems on cloud infrastructure using technologies like Docker and Kubernetes.Desirable Qualifications• Experience building user interfaces with web technologies such as React, TypeScript, etc.• Experience developing machine learning models using frameworks like PyTorch or TensorFlow.• Familiarity with SQL databases, including PostgreSQL or MySQL.• Experience with distributed model training workflows.• Strong cross-functional communication skills.• Experience working on large teams with multiple stakeholders.The base salary range for this position is $144,000 - $216,000. 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-WS1#Mid-Senior