Software Engineering Manager (Autonomous Vehicle Data)

The Role

Required Qualifications

• BA or BS in Computer Science, Electrical Engineering, Mathematics, Physics, or another relevant field; or equivalent real-world experience.
• 8+ years of professional software engineering experience in distributed systems, big data platforms, or cloud infrastructure, with 2+ years in formal engineering leadership.
• Demonstrated experience designing, operating, and scaling petabyte-scale distributed data pipelines and storage systems (e.g., Apache Spark, Beam, Ray, Flink, Kafka, Iceberg, Delta Lake, and/or Presto/Trino).
• Expert-level proficiency in C++, Rust, Go or Python, with a proven track record of designing and implementing robust, distributed systems.
• Expertise in deployment and managing services in at least one major public cloud provider (Azure, GCP or AWS) utilizing automation and observability for operational excellence
• Proven track record of translating complex, ambiguous cross-functional requirements into dependable technical roadmaps and reliable software deliverables.

Preferred Qualifications

• Experience in implementing Data Platform services including catalog, indexing, planning, pruning and optimization paths in a PB-scale environment
• Passionate about self-driving technology and its potential impact on the world.
• Comfortable delivering in a polyglot programming environment.
• Experience delivering data-intensive distributed compute architectures.

Responsibilities

• Hands-On Engineering Delivery: Serve as a player-coach, contributing directly to technical design, implementation, and delivery while mentoring and guiding the broader engineering team.
• People & Team Leadership: Recruit, mentor, and grow a high-performing team of software and data engineers. Foster a culture of engineering excellence, code quality, and ownership.
• Technical Strategy & Roadmap: Own the vision and architectural roadmap for the AV Data services including batch inference processing tools and streaming services for replay/simulation tools.
• Efficiency & Infrastructure Economics: Optimize cloud compute and storage footprint (AWS/Azure/GCP, S3 tiering, distributed caches) to balance high query performance with cost efficiencies.
• Reliability & Observability: Establish production-grade SLAs, monitoring, data lineage, and automated testing across all data services. Own incident management and root-cause analysis for mission-critical platform outages.