Full Stack Engineer Intern

Salary Range: 19 to 65 (Currency: USD) (Pay period: per-hour-wage)

Responsibilities:

Build the Metrics Visualization Hub (Full-Stack): Design and ship an end-to-end web application — front-end dashboards through back-end services — that makes autonomous-vehicle product metrics, route analytics, and pipeline health easy to explore for engineering and operations teams.

Deliver Low-Latency Data Access: Build a caching and pre-aggregation layer to reduce latency in result fetching

Optimize the Metrics Calculation Pipeline: Profile, refactor, and scale the back-end pipelines that compute product metrics, operation summaries, and route extractions — improving throughput, controlling cost, and strengthening the correctness of derived measures.

Develop the Metrics Validation Process: Build and automate validation tooling that cross-checks pipeline outputs against source-of-truth references, flags discrepancies, and gates bad data before it reaches downstream consumers — turning manual triage into a repeatable, alert-driven workflow.

Required Skills:

Full-Stack Web Development: Strong proficiency building modern web applications end-to-end — front-end frameworks (e.g., React/TypeScript) and back-end services/APIs (e.g., Python/FastAPI) — with an eye for clean, responsive, data-rich interfaces.

Backend & Data Pipeline Development: Solid programming fundamentals (e.g., Python, SQL) and experience designing, building, and maintaining data processing pipelines and the services.

Performance & Scalability: Ability to analyze code, system architecture, and data pipelines to improve execution speed and resource efficiency, including caching and pre-aggregation strategies for low-latency data access.

Preferred Skills:

Working with Large Datasets: Experience handling and processing high volumes of structured and time-series data, with strong SQL skills and familiarity with relational and/or columnar databases.

Big Data Processing: Hands-on experience with distributed processing frameworks such as PySpark for large-scale data pipelines.

Domain Exposure: Familiarity with autonomous vehicles, robotics, or other sensor/telemetry-heavy data environments.