Software Engineer, Data Enrichment

Overview

In this role you build and operate the backend and data-processing backbone for Wayve’s Data Enrichment Platform. You enable scalable, repeatable model workflows, annotation, and dataset catalogues used by model engineers. You’ll tackle large-scale inference, evaluation, and monitoring across petabyte-scale data and GPU fleets, while improving reliability, cost, and observability. You join a cross-functional team and help shape reusable platform capabilities that advance autonomous driving research and production.”,

Pay / Benefits

• Salaries benchmarked against market
• Meaningful equity
• Relocation support and visa sponsorship where applicable
• Hybrid working with core hours
• Learning and development budgets
• Comprehensive benefits including health insurance and dental

Responsibilities

• Design, build and operate backend services, APIs and data-processing workflows for the Data Enrichment Platform
• Develop model-execution workflows, versioned outputs, annotation capabilities and dataset-catalogue features
• Improve scalability and reliability for large-scale inference, automated evaluation and model monitoring
• Investigate and resolve reliability, performance and cost issues across petabyte-scale data and GPU-backed workloads
• Contribute to designs around data locality, scheduling, capacity, backpressure, failure recovery and observability
• Collaborate with model engineering, AI Platform, infrastructure, compute, storage and data teams to deliver reusable capabilities
• Write maintainable, tested and observable software, improve documentation and tooling

Key requirements

• Strong production software-engineering experience in Python, including building maintainable, tested and observable backend services, APIs or data-processing systems
• Experience designing, building or operating distributed systems for data-intensive or compute-intensive workloads
• Delivered reliable software from design through deployment and production operation
• Solid system-design fundamentals and ability to trade off decisions within a complex platform
• Comfort investigating unfamiliar systems and diagnosing reliability, performance or operational issues
• Clear communication, collaboration across teams and ownership of production components
• Experience with Flyte, Airflow, Dagster, Argo, Spark, Databricks, Ray, Kubernetes, GPU infrastructure or ML-platform systems would be advantageous
• Experience with production robotics or computer-vision platforms including annotation, inference, evaluation and model monitoring would be desirable

• Clear communication
• Cross-functional collaboration
• Ownership of production components
• Python backend services
• Distributed systems design
• Data-intensive and compute-intensive workloads

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