MBRDNA is building OneAI Products on top of the OneAI platform, creating exciting opportunities in data and backend engineering that directly power the Mercedes-Benz cars of the future. As connected vehicles generate increasing volumes of high-value data, the way this data is processed, stored, and delivered becomes business-critical. This role focuses on leveraging the latest technologies to design and scale backend services that bring AI-driven products and services seamlessly into the customer experience.
Requirements
- Distributed data systems, stream processing (Apache Flink, Spark Streaming).
- Cloud-native architectures (Azure, AWS, GCP).
- Containerized application development (Kubernetes, Docker).
- API development (Go, Java/Spring Boot).
- Advanced SQL/NoSQL data modeling and optimization.
- Infrastructure as Code and modern CI/CD pipelines.
- Security, compliance, and governance of production-grade systems.
Responsibilities
- Define and own the technical vision for large-scale backend systems, ensuring scalability, reliability, and security across millions of daily requests.
- Set enterprise-level architectural patterns and guidelines, influencing backend design across multiple teams.
- Make build-vs-buy and system evolution decisions with a long-term perspective on cost, maintainability, and innovation.
- Drive design and implementation of high-throughput, low-latency data pipelines integrating vehicle and third-party content.
- Own and evolve API ecosystems consumed by both internal platforms and external partners.
- Champion observability, reliability, and performance, enforcing SLAs and leading root-cause analysis for critical issues.
- Champion engineering excellence in coding, testing, CI/CD, and security practices.
Other
- 10+ years professional backend engineering experience (or 8+ with Master’s, 6+ with PhD).
- Proven record of leading design and delivery of large-scale, distributed backend systems in production.
- Lead multi-team projects and deliver strategic impact.
- Make high-judgment technical tradeoffs balancing innovation, scalability, and cost.
- Influence at executive and global engineering leadership levels.
