Job Description
This software engineer develops platforms for machine learning data labeling, building services that handle high-volume data operations essential for autonomous vehicles. The work advances self-driving technology, creating safer mobility solutions for drivers, logistics firms, and urban residents reliant on efficient transportation. Engineers apply production practices like Kubernetes to ensure reliable systems that interface with various autonomy consumers, improving data quality for better vehicle performance.
Aurora develops self-driving technology to transform mobility and logistics, aiming for a safer, efficient, and accessible future. The company operates with teams tackling complex problems in data engineering, building end-to-end toolchains from collection to evaluation. Aurora hires talented individuals with diverse backgrounds, focusing on innovation without artificial hierarchies.
Engineers work on feature development for labeling applications, designing processes to enhance data coverage, and collaborating across product and data science teams. They use tools like AWS, Kubernetes, Docker, and programming languages such as Python and C++. Interactions include coordinating with program, operations, and data science functions. A constraint is meeting mission-critical project timelines while managing high-volume service demands in a collaborative environment.
The base salary ranges from $126,000 to $201,000 annually, determined by role, level, and location, with adjustments based on skills and experience. Eligible for annual bonus and equity compensation. Benefits include comprehensive health coverage and additional perks promoting well-being. Work arrangements offer flexibility, though specific location and schedule details depend on individual assignments. Professional development includes access to resources for career growth in a diverse, inclusive setting.
Responsibilities
- Apply production best practices to ensure platform reliability
- Feature development for new labeling applications
- Collaborate across teams and functions (product, program, operations, data science) to drive projects from inception to delivery
- Apply ML Ops best practices to design processes for improving data coverage and quality
Requirements
- BS / MS / PhD degree in Computer Science or a related field
- For non-CS majors or BS candidates, strong software experience (4+ years in industry)
- 4+ years of expertise with production backend services (Cloud, Kubernetes, DevOps)
- Experience with C++ and Python
- Relevant domain knowledge: data annotation, autonomous driving, computer vision, robotics, mapping, numerical computing, 3D graphics
- Specific technology: AWS, Terraform, Bazel, Docker, Kubernetes, gRPC, SQL, Elasticsearch
- Supplementary programming languages: Golang, Typescript