Role Description We are looking for a Senior Full Stack AI Engineer to design, build, and scale production-grade applications powered by generative AI. This role is focused on delivering real products, not just prototypes. The ideal candidate is a strong end-to-end builder who can make sound architectural decisions, integrate AI capabilities into usable systems, and balance fast delivery with long-term maintainability. - Build end-to-end applications that incorporate generative AI capabilities into real user workflows - Develop scalable backend services, APIs, and integrations using modern engineering practices - Build responsive and maintainable frontend experiences using modern frameworks - Design modular system architectures that support scalability, reliability, and long-term maintainability - Integrate LLMs and other AI tools into production applications with appropriate safeguards and monitoring - Optimize prompt flows, data pipelines, and system interactions for consistency and performance - Translate business requirements into technical solutions and make strong implementation trade-offs - Own features from ideation through development, testing, deployment, and iteration - Reuse and adapt proven solutions where appropriate to accelerate delivery - Continuously improve product quality based on performance data, feedback, and operational learnings Qualifications - Strong full stack engineering experience, ideally 5+ years - Proficiency in backend development with technologies such as Node.js, Python, or similar - Strong frontend experience with frameworks such as React or comparable modern tools - Experience building and consuming APIs, working with databases, and deploying on cloud platforms - Hands-on experience integrating generative AI into production applications - Practical understanding of prompt engineering and AI system limitations, including failure modes and edge cases - Proven ability to design systems that scale beyond MVP and remain maintainable over time - Strong understanding of software architecture, system design, and engineering best practices - Demonstrated experience shipping multiple production-grade products or platforms - Ability to combine traditional software engineering discipline with modern AI-driven workflows - Experience working independently with strong ownership and product mindset Benefits - Opportunity to build impactful AI-powered products used in real production environments - High ownership across architecture, implementation, and delivery - Role suited for engineers who enjoy both product thinking and technical depth - Chance to shape scalable systems early and influence long-term engineering quality - Fast-paced environment with a strong emphasis on execution and practical outcomes - Meaningful work at the intersection of full stack engineering and applied AI

• Define and drive a long-term AI/ML architectural vision • Own the strategic roadmap for Twilio’s ML/AI Ops platform and tooling • Evaluate and implement modern LLM architectures • Lead architecture for agentic AI systems • Transition seamlessly from high-level strategic communication to deep-dive code reviews • Partner closely with Product Management

• Develop and implement scalable and performant solutions. • Partner, as a peer, with Engineering Managers, Product Managers, and stakeholders throughout Aledade to develop and execute technical roadmaps using Agile processes. • Mentor and coach more junior engineers including thorough pull request reviews for other developers and be receptive to critical feedback on your own work.

• Partner with engineering leadership to define Tailscale's internal AI-enablement priorities and roadmap. • Work with the team that's building Aperture by Tailscale – our AI gateway. • Build, maintain, and iterate on internal tools, workflows, and shareable practices. • Evaluate new AI models, agents, and tools. • Work directly with both engineering and non-engineering teams to find high-leverage opportunities. • Develop infrastructure, guardrails, review practices, and documentation for safe AI use. • Act as an internal advocate for effective AI practices. • Track adoption and measure the impact of AI tooling on engineering velocity.