Before the detail, here's the challenge you'd help us solve. We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future. Very few people in AI can say this. Every role here, whatever the team, plugs into that.
🏢 About Wayve
Wayve is building the leading AI platform for autonomous driving. We are pioneering an end to end AI approach that enables vehicles to learn directly from real world experience, developing the ability to adapt and improve over time.
🎯 The Role
As a Staff Software Engineer on the Model Development Platform, you'll own the architecture of the cloud services that power Wayve's AI model lifecycle, from data and training workflows to experiment scheduling, evaluation and on-road testing. You'll build the secure, reliable and scalable foundations (identity and access, microservices, micro-frontends, deployment and rollback) that let researchers and engineers iterate fast and ship autonomous driving models safely.
✅ Key Responsibilities
Scalable, reliable cloud services: Design and evolve the architecture of major platform services for reliability, observability, security and scalability. Help set availability, latency and performance targets (SLAs/SLOs) and drive the practices to meet them.
Authentication & authorization: Own the identity, access control and auditability layer across platform services and applications, so access to sensitive models and fleet data is secure, consistent and easy to adopt.
Microservices & micro-frontends: Define the patterns, boundaries and shared tooling for backend microservices and micro-frontends, so teams can build and release independently while systems interoperate cleanly.
Deployment & rollback infrastructure: Build robust CI/CD, progressive delivery and fast, safe rollback, with the observability, alerting and self-healing needed to ship often and recover quickly.
End-to-end technical ownership: Take significant services and multi-quarter initiatives from problem definition and design through to production operation, making clear trade-offs between speed, scalability, security and operational simplicity.
Cross-team technical leadership: Connect your area with adjacent disciplines, from experiment scheduling and data pipelines to distributed compute, and lead design reviews that establish reusable patterns.
Hands-on problem solving: Tackle the hardest technical challenges in your subteams with pragmatic solutions that balance innovation and operational simplicity.
Strategic collaboration: Partner with Product, Research, Design and Operations to align technical solutions with user needs and contribute to the platform's long-term roadmap.
📌 Required Qualifications
Technical leadership: 7+ years designing and building large-scale distributed systems, cloud platforms or developer platforms, including at senior or staff level.
Cloud services & architecture: Proven ability to design and operate scalable microservice architectures on a major cloud provider, with strong grounding in API design, data modeling, asynchronous workflows and distributed-systems fundamentals.
Security & access control: Hands-on experience implementing authentication and authorization (e.g. OAuth2/OIDC, RBAC) with sound security practices.
Reliability & delivery: Experience driving reliability improvements, defining SLAs/SLOs, and building observable, self-healing, highly available systems. Experience with CI/CD, containerization and orchestration (e.g. Kubernetes), infrastructure as code, and safe release and rollback strategies.
Hands-on engineering: Ability to write and review production-quality code in languages such as Python or TypeScript, with a good understanding of modern web architectures including React and micro-frontends.
End-to-end ownership: A track record of taking significant services from design through deployment and production operation.
Collaborative influence & mentorship: Excellent communication and cross-functional skills, with a track record of mentoring engineers through design discussions, code reviews and pairing, and of fostering a culture of engineering excellence.
⭐ Desirable Experience
Experience building internal developer platforms, workflow systems or tools for ML researchers and engineers.
Familiarity with ML operations, such as training, evaluation, experiment tracking and artifact management.
Experience with data pipelines, distributed job orchestration, event-driven architectures or large-scale compute (e.g. Spark, Ray, Airflow, MLflow).
Experience with service mesh, API gateways, or feature-flagging and progressive-delivery tooling.
Knowledge of privacy, compliance and secure handling of sensitive or large-scale data.
Experience in autonomous systems, robotics, simulation or another safety-critical domain.
