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About the Role
As a Staff Software Engineer for the Data Enrichment Platform, you will own the technical vision for systems that enable Wayve’s teams to deploy, run, evaluate, and continually improve machine-learning models. You will shape a platform that processes petabytes of driving data, operates across large GPU fleets, and supports dozens of model engineers, creating dependable, self-service capabilities that improve model quality while reducing cost and operational effort.
Responsibilities
- Define the technical vision and architecture for Wayve’s end-to-end Data Enrichment Platform.
- Lead the development of backend services, APIs, model-execution workflows, versioned outputs, annotation capabilities, and dataset catalogues.
- Build scalable and reliable systems for large-scale inference, automated evaluation, model monitoring, active learning, and retraining.
- Partner with platform teams to address data locality, scheduling, capacity, backpressure, failure recovery, observability, and cost across petabyte-scale datasets and large GPU fleets.
- Make effective build, buy, reuse, integrate, consolidate, and replace decisions as Wayve’s requirements and the technology landscape evolve.
- Establish clear technical boundaries and working relationships across AI Platform, infrastructure, compute, storage, data, and model-engineering teams.
- Remain hands-on while leading cross-team delivery, mentoring engineers, and driving the adoption of reusable, self-service platform capabilities.
- Define measurable service levels and success metrics covering platform reliability, throughput, cost, automation, and adoption.
Requirements
- Experience owning the long-term technical direction and measurable outcomes of a complex, multi-system platform or capability.
- Strong production software-engineering experience in Python, including 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.
- A track record of productizing internal platforms for broad adoption, with a focus on reliability, usability, and self-service.
- Excellent architecture and technology judgment, combined with the ability to remain hands-on and lead delivery across organizational boundaries.
- Strong communication and influencing skills, with experience mentoring senior engineers and aligning multiple technical teams.
- Experience with workflow-orchestration technologies such as Flyte, Airflow, Dagster or Argo.
- Experience building large-scale batch-inference or ML-platform systems, including model deployment and model registries.
- Knowledge of Spark, Databricks, Ray or comparable distributed-compute technologies.
- Experience with annotation or dataset-catalogue platforms, including metadata, provenance, versioning, lineage, and quality control.
- Experience with Kubernetes, GPU infrastructure, and cost-aware cloud architecture.
- Experience implementing continuous evaluation, model-quality monitoring, active-learning, or automated retraining workflows.
- Experience designing or operating production platforms for robotics or computer-vision workloads, with an understanding of how data, annotation, inference, evaluation, and monitoring fit together.
Skills
- Python
- Backend services
- APIs
- Data-processing systems
- Large-scale distributed systems
- Workflow-orchestration technologies
- ML-platform systems
- Model deployment
- Model registries
- Spark
- Databricks
- Ray
- Distributed-compute technologies
- Annotation platforms
- Dataset-catalogue platforms
- Kubernetes
- GPU infrastructure
- Cloud architecture
- Continuous evaluation
- Model-quality monitoring
- Active-learning
- Automated retraining workflows
- Robotics
- Computer-vision
Experience Level
- Staff Software Engineer