Platform Engineer, AI Enablement

Wayve · London

  • Junior
  • Full-time
  • Posted 2026-09-09
  • Confirmed live on 25 September 2026

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Job description

About us

Founded in 2017, Wayve is the leading developer of Embodied AI technology. Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems.

Our vision is to create autonomy that propels the world forward. Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving.

In our fast-paced environment big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future.

At Wayve, your contributions matter. We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact.

Make Wayve the experience that defines your career!

The role

As a Platform Engineer on Wayve’s AI Enablement team, you’ll build the infrastructure that gives people across the company safe, reliable, and cost-effective access to language models and AI agents.

You’ll help create a governed model-access layer and a secure production environment for agentic workflows, with safety, observability, and operational excellence built in from the start. Your work will span the full lifecycle—from defining problems and designing systems to implementation, deployment, and ongoing operation.

Working closely with Security, IT, and engineering teams, you’ll deliver platform capabilities that scale with the pace of AI adoption at Wayve. You’ll also support teams building agentic workflows, turning successful approaches into reusable patterns and helping colleagues understand what’s possible with the platform.

This is an opportunity to join an early platform team and shape how an AI company puts AI tools to work responsibly. Your technical decisions will influence infrastructure used across Wayve, giving you ownership, visibility, and impact beyond a typical infrastructure role.

Key responsibilities

• Design, build, and operate infrastructure that enables safe and cost-effective access to AI models and tools.

• Build the runtime, services, and developer tooling used to run agentic workflows in production.

• Create observability across cost, performance, reliability, and usage through metrics, tracing, and logging.

• Implement governance and safety controls that make AI use secure, compliant, and auditable.

• Develop reusable platform primitives, integrations, and guardrails that reduce one-off solutions.

• Contribute to standards and best practices for designing, building, and deploying agents.

• Automate infrastructure and deployment processes to keep the platform reliable and scalable.

• Partner with Security, IT, and engineering teams on platform integration, policy, and rollout.

• Help teams adopt the platform by troubleshooting issues, documenting patterns, and sharing what works.

• Educate colleagues on the AI platforms and tools available to them and how to use them effectively.

• Own projects end to end and improve the platform based on real-world usage and feedback.

• Participate in the team’s on-call rotation, providing out-of-hours support for critical systems when required.

About you

You’re a hands-on engineer who enjoys building reliable platforms for other teams. You’re comfortable navigating ambiguity, taking ownership across the full delivery lifecycle, and collaborating with technical and non-technical partners. You combine strong engineering fundamentals with curiosity about the fast-evolving AI tooling ecosystem.

Essential skills and experience

• 3+ years of software engineering experience building APIs, services, and developer tooling.

• Strong system-design fundamentals and experience making infrastructure decisions that affect multiple teams.

• Experience building and operating platforms or infrastructure used by other engineering teams.

• Hands-on experience with cloud infrastructure, Kubernetes, and infrastructure as code.

• Familiarity with LLM APIs and the surrounding ecosystem, including model providers, gateways, MCP, agent frameworks, and retrieval-augmented generation.

• Experience building observability into distributed systems using metrics, tracing, and logging.

• A security-conscious approach, including experience with authentication, authorization, and auditability.

• A strong sense of ownership and comfort working in an ambiguous, fast-moving environment.

• Clear communication and documentation skills that enable other teams to self-serve.

Desirable skills and experience

• Experience operating an LLM gateway or model-routing layer in production, including cost controls and budget enforcement.

• Experience with agent orchestration frameworks or tool-calling protocols such as MCP.

• Exposure to AI governance, model risk manage

Prepare for the interview

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