Overview
In this role you will own the technical vision for Wayve’s Data Enrichment Platform, shaping how model engineering, infrastructure, compute and data work together to accelerate development. You’ll lead end-to-end backend services, APIs, and model-execution workflows, ensuring scalable, observable systems on petabyte-scale data and large GPU fleets. You’ll balance long-term platform direction with hands-on delivery while mentoring engineers and aligning multiple teams around clear ownership. A core goal is to enable self-service workflows and measurable improvements in reliability, cost, and throughput. This is a mission-driven opportunity to influence how Wayve scales autonomous-driving AI.
Pay / Benefits
- salary benchmarked against market
- meaningful equity
- relocation support and visa sponsorship
- hybrid working with core hours
- learning and development budgets
- comprehensive health and wellbeing benefits
Responsibilities
- Own the technical vision and architecture for the end-to-end Data Enrichment Platform
- Lead design, development and operation of backend services, APIs, model-execution workflows, versioned outputs, and dataset catalogues
- Make architectural decisions for petabyte-scale data and large GPU fleets (data locality, scheduling, backpressure, failure recovery, observability, cost)
- Evaluate build/buy/reuse/integrate options to meet evolving needs
- Define boundaries and ownership across model engineering, AI Platform, infrastructure, compute, storage and data teams
- Remain hands-on while leading cross-team delivery and raising standards for maintainable, tested software
- Lead and develop a small team (2–3 engineers) with coaching and performance feedback
- Drive adoption of reusable, self-service capabilities for independent workflows
- Define measurable SLAs and success metrics (reliability, throughput, cost, automation, adoption)
Key requirements
- Owned long-term technical direction of a complex, multi-system platform
- Strong production software-engineering experience in Python for backend services, APIs and data-processing systems
- Deep experience designing and operating large-scale distributed systems for data- or compute-intensive workloads
- Track record turning internal platforms into widely adopted, reliable self-service products
- Excellent architecture and technology judgement; balance delivery with long-term direction
- Hands-on leadership across organizational boundaries and ability to align teams around ownership/shared outcomes
- Experience mentoring senior engineers and strengthening technical capabilities
- People leadership experience (managing or leading a small team of 2-3 engineers)
- Experience with workflow orchestration, ML platforms, model deployment or registries is advantageous
- Tech stack exposure: Flyte, Airflow, Dagster, Argo, Spark, Databricks, Ray, Kubernetes; GPU infrastructure
- Experience with annotation or dataset-catalogue platforms (provenance, versioning, lineage, quality control) is valuable
- Experience with production robotics or computer-vision platforms (annotation, inference, evaluation, model monitoring) is desirable
- Computer-vision research expertise not required
- Clear communicator with ability to mentor and influence senior engineers
- Cross-team collaboration and stakeholder management
- Strategic thinking with practical execution and ownership mindset
- Python backend development
- Distributed systems design and operation
- Workflow orchestration and ML platforms
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