SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Wayve is building the leading AI platform for autonomous driving using end-to-end AI that enables vehicles to learn from real-world experience. The company is pioneering a mapless, hardware-agnostic approach that integrates with global OEM partners.
As Staff Software Engineer for the Data Enrichment Platform, you will own the technical vision and long-term direction of the Enrichment Orchestration team's platform. This platform enables Wayve's teams to deploy, run, evaluate and continually improve machine-learning models, processing petabytes of driving data across large GPU fleets.
Your responsibilities include:
- Owning the technical vision and architecture for the end-to-end Data Enrichment Platform, translating user needs into clear technical direction
- Leading design, development and operation of backend services, APIs, model-execution workflows, versioned outputs, annotation capabilities and dataset catalogues
- Making architectural decisions for petabyte-scale data and large GPU fleets, addressing data locality, scheduling, capacity, backpressure, failure recovery, observability and cost
- Evaluating when to build, buy, reuse, integrate, consolidate or replace technologies as needs evolve
- Establishing clear technical boundaries and ownership across model engineering, AI Platform, infrastructure, compute, storage and data teams
- Remaining hands-on while leading cross-team delivery, mentoring senior engineers and raising standards for maintainable, tested and observable software
- Driving adoption of reusable, self-service capabilities so teams can onboard and operate workflows independently
- Defining measurable service levels and success metrics covering reliability, throughput, cost, automation and adoption
You will work on a scalable platform for large-scale inference, automated evaluation, model monitoring, active learning and retraining. The platform includes self-service backend services and APIs enabling dozens of model engineers to deploy and operate repeatable workflows independently, reproducible dataset selection with versioned outputs and annotation workflows, reliable model-execution workflows operating across petabytes of driving data and large GPU fleets, and monitoring and operational tooling for visibility into model quality, platform reliability, throughput, cost and workflow status.
Wayve operates a hybrid working model combining in-person collaboration in London with focused remote work. The company has additional hubs in Sunnyvale, Yokohama, Herzliya, Vancouver and Leonberg.
REQUIREMENTS:
- Demonstrated ownership of the long-term technical direction of a complex, multi-system platform or capability with measurable outcomes
- Strong production software-engineering experience in Python, building maintainable, tested and observable backend services, APIs and data-processing systems
- Deep experience designing and operating large-scale distributed systems for data-intensive or compute-intensive workloads
- Track record of turning internal platforms into widely adopted, reliable and usable self-service products
- Excellent architecture and technology judgement, balancing immediate delivery needs with long-term platform direction
- Ability to remain hands-on while leading delivery across organisational boundaries, influencing technical decisions and aligning teams around clear ownership and shared outcomes
- Strong communication skills and experience mentoring senior engineers and strengthening technical capabilities of those around you
- Advantageous: experience with workflow orchestration, batch inference, ML platforms, model deployment or registries (Flyte, Airflow, Dagster, Argo, Spark, Databricks, Ray, Kubernetes, GPU infrastructure)
- Advantageous: experience with annotation or dataset-catalogue platforms including provenance, versioning, lineage and quality control
- Particularly desirable: experience with production robotics or computer-vision platforms including annotation, inference, evaluation and model monitoring
