Backend Engineer - Metrics Infrastructure (Analytics)
About the Team
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.
About the Role
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.
What You’ll Do
- 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.
What You’ll Need
- 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(nlogn)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).
Nice to Have
- 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).
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.