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Torc, now part of the Daimler family, is a leader in autonomous driving software for commercial trucks. The Data Engineering team builds secure, scalable systems enabling customers to work with data confidently. This role focuses on implementing and maintaining AWS-native data ingestion, ETL, and storage solutions that power analytics, simulation, and ML training across the enterprise.
Key responsibilities include: designing and building robust, resilient pipelines to process massive daily volumes of data from vehicle fleets and simulation environments; scaling Torc's data lake through distributed storage, data crawling, and discovery; ensuring data integrity through validation, versioning, data provenance, and governance; acquiring and integrating additional datasets from internal and external sources; contributing to cloud-based solution architecture and delivery; collaborating with perception, planning, control, mapping, and vehicle testing teams; and supporting emerging cloud capabilities for safety-critical autonomous vehicle software.
The team operates as distributed and remote-first, with daily standups, planning sessions, and ad hoc collaboration via Zoom and Slack. Engineers own services end-to-end and participate in business-hour on-call rotation. The company prioritizes healthy work/life balance and sustainable development.
Required qualifications: Bachelor's in Computer Science, Robotics, Electrical Engineering, or related field plus 4+ years of experience (or Master's plus 0-3 years). Must have hands-on experience with Linux systems deployment, troubleshooting, and maintenance; public cloud workloads; database architectures (relational, NoSQL, vector stores, data warehousing, distributed stores); Linux and bash scripting; Docker and containerization; workflow patterns using DAGs (Apache Airflow, AWS Step Functions); test-driven development, CI/CD, and infrastructure as code.
Bonus experience includes Databricks, Ray for ML scaling, pandas/numpy, AWS serverless architectures (Lambda, Batch, ECS Fargate, Glue, Athena), and robotics/ADAS data patterns. Tech stack: AWS managed services, Linux, Bash, Docker, Terraform, Python, React/JavaScript, Datadog, AWS CloudWatch, Jenkins, GitHub Actions.
