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We are hiring an Engineering Manager (M4) to lead the team responsible for both semantic enrichment pipelines and the final silver and gold layers of the Wayve Corpus. This team transforms multimodal driving data and perception model outputs into reliable, high quality data products used across autonomy, evaluation, simulation, and research. The role combines high scale ML in the loop enrichment pipelines such as semantic segmentation, cuboid annotation, embeddings, and BC and ODD signals with production grade data engineering ownership including schema governance, table interfaces, quality gates, lineage, and SLO based operations. You will lead a team of up to 10 engineers across ML engineering, perception, and data engineering. You will own a multi quarter roadmap that scales enrichment throughput, improves data quality, and hardens corpus tables used across Wayve. You will partner with application, model training, and evaluation, teams to ensure alignment on requirements and interfaces. This role requires a leader comfortable at the intersection of ML systems and data engineering who can provide clear direction, reliable delivery, and strong people leadership during a period of significant technical and organizational scaling.
Job Responsibility:
Lead, coach, and grow a team of up to 10 engineers across ML engineering, perception, and data engineering
Define team structure, roles, leveling, hiring needs, and long term growth plans
Own and scale semantic enrichment pipelines including semantic segmentation, cuboids, embeddings, scenario, and ODD classification
Integrate ML assisted labeling, validation, and automated quality checks into enrichment workflows
Own the silver and gold layers of the Wayve Corpus including schema evolution, versioning, documentation, lineage, observability, and SLO backed operations
Establish data quality gates and quality metrics for enriched and corpus level data
Deliver a multi quarter roadmap spanning enrichment and corpus systems with predictable execution
Lead architecture decisions to improve efficiency, maintainability, and reliability
Partner with Data Platform on distributed compute systems including Spark, Databricks, Ray, and Flyte
Align with autonomy, evaluation, and research teams on corpus requirements, interfaces, and lifecycle
Requirements:
2+ years managing engineering teams in ML systems, perception, or large scale data infrastructure
Experience delivering ML in production or perception pipelines, or strong experience in production data engineering systems. Ideally exposure to both
Proven ownership of production data tables such as Delta Lake, Spark, Hive, or BigQuery including schema evolution and multi team consumers
Experience with distributed compute systems such as Spark, Databricks, Ray, or Flyte
Experience building observable, high throughput pipelines
Ability to lead multi quarter delivery, manage dependencies, and align with multiple stakeholders
Strong communication and cross functional collaboration skills
Nice to have:
Experience with multimodal perception data such as images, video, and LiDAR
Experience with annotation workflows or ML assisted labeling systems
Experience with embeddings, feature stores, or ML data layers
Familiarity with data quality frameworks and operational analytics
Experience in autonomous vehicles, robotics, or large scale computer vision systems
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