Backend Engineer - Metrics Infrastructure (Analytics)

We build infrastructure and tools that power metrics for autonomous vehicles and delivery robots. Our goal is to shorten the feedback loop: make it straightforward to develop, run, and use metrics at scale. Metric calculations are often complex compute graphs (multiple languages) with heavy compute; across millions of miles and simulations, they produce terabytes of results. A key part of the job is choosing storage layouts and schemas that keep inserts efficient and reads fast.

You will design, develop, and operate the platform for metrics: data models and storage, scalable pipelines, and a developer-friendly framework for writing, testing, and shipping metrics. The role includes close collaboration with metric authors and with metric users—development, analytics, and QA—and ensuring reliable end-to-end deliveryof metric results.

• Own the metrics platform: clear schemas, storage/layouts for efficient inserts and fast reads, simple versioning.
• Build and maintain the framework for writing/running metrics (interfaces, examples, local run, CI/compat checks).
• Create a test system for metrics and pipelines (unit / contract / regression on synthetic and sampled data).
• Operate the compute and storage paths in production; monitor, debug, and keep the system stable and cost-aware.
• Partner with metric authors and with development/analytics/QA to plan changes and land them safely.

• Python in production, including async (e.g., asyncio, aiohttp, FastAPI).
• Strong SQL (JOINs, window functions, CTEs); ability to read plans and speed up slow queries.
• Data structures & algorithms—know when O(nlog⁡n)O(n \log n)O(nlogn) matters and choose the right structures.
• Experience with databases: PostgreSQL, ClickHouse; understanding OLAP vs. OLTP trade-offs.
• Workflow orchestration experience (Airflow / Argo / Prefect / Dagster—any is fine).
• Data libs & validation: NumPy, pandas, Pydantic (or equivalents).
• Containers & orchestration: Docker, Kubernetes.
• Experience building web services (Django / FastAPI / Flask—stack flexible).

• C++ exposure (reading, small changes, or components in the compute graph).
• Infrastructure for ML-adjacent metrics/evaluation.
• Parquet & object-storage layout/partitioning; Kafka/task queues; basic observability (logs/metrics/traces).