• Develop Real-World Software: Build applications for diverse industries, including QA traceability, B2B commerce, financial services, and home energy management. • Manage the Full Software Lifecycle: Participate in all phases, from requirements gathering and team setup to architecture design, implementation, and production release. • Master New Technologies: Learn and apply emerging concepts to solve complex client challenges. • Ensure Code Quality and Documentation: Take ownership of code and documentation that reaches the main branch. • Drive Process Improvement: Identify and implement opportunities to enhance team and company workflows.

• Deliver high-quality work on time, leveraging AI tools to accelerate velocity without sacrificing craft. • Own feature delivery end-to-end, from scoping and design input through implementation and release, with minimal oversight. • Craft well-tested software that meets a high bar for quality, using AI-assisted development practices where they add value and exercising judgment where they don't. • Build and validate AI-integrated features with sound engineering judgment, applying appropriate evaluation practices to LLM-powered and AI-assisted functionality. • Partner closely with Engineering, Product Management, and Product Design to refine ambiguous work into clearly defined, valuable deliverables. • Maintain reliable systems by applying best practices for observability, quality, and incident response, and actively use AI tooling to detect, diagnose, and resolve issues faster. • Support production releases, ensuring your team's systems are deployment-ready and stable post-release.

• Design and champion internal Quality Programmes: Drive engineering-wide process changes and ensure pods adopt new reliability standards without friction. You will operate with a product-owner mindset to define and evangelize the internal reliability roadmap. • Define and enforce hard metrics: Measure and establish hard quality telemetry (e.g., DORA metrics, bug-to-deploy ratios, SLIs/SLOs) to create a quantifiable baseline for platform reliability. • Architect Shift-Left Pipeline Gates: Weave automated Performance, Security, and Accessibility checks directly into the CI/CD pipeline at the PR level in tight partnership with Eng Core. • Build Real-World Load Testing: Shift load and performance testing left for every customer onboarding, validating real-world assumptions. • Engineer Synthetic Data & Production Canaries: Build the architecture for safe synthetic data injection to unblock heavy load-testing and live-production canaries, strictly isolating test data from authentic user telemetry. • Leverage Generative AI Tooling: Actively utilize AI assistants (e.g., Gemini, Claude, Cursor, Codex) to accelerate the development of testing frameworks, automate infrastructure code, and design advanced testing architectures. • Drive Tooling Consolidation: Lead the technical migration away from expensive, legacy testing infrastructure to a unified, AI-supported automation stack. • Help define and maintain development practices: Enable fast iteration while ensuring quality, including writing tests and documenting key implementations.

• Join the team as Twilio’s next Principal Software Engineer. • This position is needed to contribute to Twilio's engineering projects. • Collaborate with internal teams and participate in meaningful projects.