ML Platform Engineer Intern: Build Scalable Data Pipelines

Aurora; San Francisco (CA); On-site; USD 72,000 - 80,000; Full time

Job summary

Aurora is seeking a Summer 2027 Data & ML Platform Engineering Intern to help build infrastructure that enables ML at scale. You will work on data pipelines, ML tooling, and cloud-scale data processing.

You will collaborate with ML engineers to turn sensor and behavioral data into model-ready datasets, while advocating for observability and production readiness. This role requires ongoing studies in CS or related fields and hands-on backend experience.

Qualifications

• Currently pursuing a Bachelor's or Master's degree in Computer Science, Data Engineering, or a related field.
• Strong programming skills in Python and/or Golang.
• Working knowledge of data pipeline/ETL concepts and basic distributed systems tradeoffs (e.g. CAP theorem).
• Experience with cloud platforms (AWS or GCP).
• Some applied exposure to ML, robotics, maps/geospatial systems, or a comparable domain.
• Hands-on experience building or extending a backend system or pipeline with real scale-up/scale-out requirements.

Responsibilities

• Build and maintain data pipelines and workflow automation that turn raw fleet and sensor data into model-ready datasets.
• Design or extend ML platform tooling (labeling systems, data engines, distributed compute) used across autonomy teams.
• Use cloud infrastructure (AWS/GCP) to scale data ingestion, storage, and processing workloads.
• Partner with ML engineers to understand downstream model requirements and translate them into pipeline/platform design decisions.
• Instrument systems for observability and help harden a prototype into something that can run in production.

Skills

Python

Golang

Data pipelines

Cloud platforms

Distributed systems

ML basics

Education

Bachelor's or Master's in CS/Data Eng

Tools

AWS

GCP

Spark

Job description

Aurora is seeking a Summer 2027 Data & ML Platform Engineering Intern to help build infrastructure that enables ML at scale. You will work on data pipelines, ML tooling, and cloud-scale data processing.

You will collaborate with ML engineers to turn sensor and behavioral data into model-ready datasets, while advocating for observability and production readiness. This role requires ongoing studies in CS or related fields and hands-on backend experience.