Senior Software Engineer - Engineering Productivity Systems

About the Team

Our team is responsible for building the internal systems for the whole engineering team. Our goal is to help engineers navigate across all produced data, investigate problems, and create ad hoc analytics. Our services are used daily across the company: by QA engineers verifying component-level quality, by autonomy engineers investigating unexpected behavior, by ML teams validating new model versions, by safety analysts and data scientists who analyze thousands of scenarios and metrics to assess system readiness, and by release engineers who test our products on the public roads.

About the Role

As a Productivity Engineer, you'll design, build, and optimize the systems that power our services. For example, we have the Simulator as a service. This service helps engineers to reproduce scenarios from real-world rides and calculate metrics. You will build tools to help with mining scenarios, organize them, track simulations, and collect metrics. Another example is MLFlow. We use our own fork, focusing on performance. We need to process hundreds of millions of metrics every day, produced by the training of the ML models.

What You'll Do

• Architecting and implementing backend services for different purposes.
• Designing efficient data storage using systems like S3, Postgres, ClickHouse, and Redis.
• Defining infrastructure as code using Terraform to automate deployment and scaling in AWS clusters.
• Collaborating with simulation engineers, the analytics team, and infrastructure teams to ensure robust integration and performance.

What You'll Need

• 3+ years of experience building backend services in production environments.
• Proficiency in at least one of the following languages: Python (preferred), Java, or Go.
• Understanding of cloud-native architecture and distributed systems.
• Hands-on experience with key technologies such as AWS (S3, EKS), Postgres, and Terraform.
• Experience with containerization and orchestration (e.g., Docker, Kubernetes).

Candidates are required to be authorized to work in the U.S. The employer is not offering relocation sponsorship, and remote work options are not available.