Autonomous Driving Data Pipeline Engineer

Wayve; Ogle Township (PA); On-site; USD 93,000 - 128,000; Full time

Benefits offered by this job

Hybrid work model

Office in Leonberg

Job summary

Wayve in Leonberg is seeking a Data Engineer to design and deliver scalable data pipelines for model development and autonomous driving workloads. You will ingest, transform and curate large-scale real-world, synthetic, and partner datasets, ensuring data quality and readiness for ML use cases.

You'll work closely with ML engineers, Data Corpus teams, and external partners to optimize performance and reliability, enabling rapid iteration across production ML systems in a hybrid office setting.

Qualifications

• Proven experience building and operating scalable data pipelines in production.
• Strong software engineering practices with maintainable, well-tested code.
• Proficient in SQL and PySpark, with data warehousing concepts.
• Experience with DAG-based systems like Airflow, Flyte, or Ray.
• Understanding of robotics and autonomous driving data concepts.
• Familiarity with ML data workflows and model iteration.
• Excellent communication and collaboration across teams.

Responsibilities

• Build and improve scalable data pipelines for model development and ML workflows.
• Ingest, transform, and curate large datasets for structured model-ready formats.
• Create data quality checks and monitoring for raw and processed data.
• Curate real-world and synthetic data to enhance scenario diversity.
• Improve pipeline performance, reliability, and usability.
• Collaborate with ML engineers and external partners to integrate with production systems.

Skills

Python

PySpark

SQL

Data Pipelines

Airflow

Workflow Orchestration

OLAP / Warehouse Concepts

Collaboration

Tools

Airflow

Flyte

Ray

Spark

Job description

Wayve in Leonberg is seeking a Data Engineer to design and deliver scalable data pipelines for model development and autonomous driving workloads. You will ingest, transform and curate large-scale real-world, synthetic, and partner datasets, ensuring data quality and readiness for ML use cases.

You'll work closely with ML engineers, Data Corpus teams, and external partners to optimize performance and reliability, enabling rapid iteration across production ML systems in a hybrid office setting.