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Rivian and Volkswagen Group Technologies

Software Engineer II - Streaming

Palo Alto, California (Remote)$116.3k–$154.1kfulltimemidAdded today

About this role

Join Rivian and Volkswagen Group Technologies' Data Platform Streaming team to design and operate scalable, real-time streaming infrastructure powering vehicle telemetry, analytics, and AI/ML platforms. This hands-on Software Engineer II role focuses on building fault-tolerant, low-latency systems using Apache Kafka/Redpanda and Flink in a cloud-native environment.

What you'll do

  • Build and maintain scalable streaming services ingesting and processing vehicle, cloud, and operational data at scale
  • Design and develop Apache Flink applications and event-driven services using Kafka or Redpanda
  • Create streaming infrastructure supporting analytics, machine learning, and real-time decision-making
  • Develop production-grade streaming applications in Java, Scala, Python, or Go with emphasis on performance and reliability
  • Build observability and monitoring into streaming systems using metrics, logging, distributed tracing, and alerting
  • Partner with platform, data, ML, and analytics teams to deliver end-to-end real-time solutions

What they're looking for

  • Apache Kafka or Redpanda
  • Apache Flink
  • Distributed systems design
  • Java, Scala, Python, or Go
  • Kubernetes and Docker
  • Event-driven architecture
  • Performance optimization and tuning
  • CI/CD pipelines and Infrastructure as Code
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Rivian and Volkswagen Group Technologies

Rivian and Volkswagen Group Technologies is developing next-generation software-defined electric vehicles through a joint venture, focusing on systems architecture, battery management, and safety-critical systems. The company is hiring engineers in software architecture, embedded systems, functional safety, test integration, and audio systems to build reliable and compliant EV platforms.

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Likely interview questions

  • Describe your experience designing topic partitioning strategies and replication schemes in Kafka for high-throughput scenarios
  • How have you optimized Flink applications for latency, throughput, and resource utilization in production?
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