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 delivery of 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, aio 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).

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.