Software Engineer (AI-native development)

Working with minimal supervision, you will partner with product managers, technical leads, and fellow engineers to understand customer and business requirements and translate them into secure, reliable, scalable, and maintainable solutions. As a Software Engineer, you will contribute across the engineering lifecycle for a set of product features and services - designing, coding, testing, deploying, and operating solutions that are secure, reliable, accessible, and maintainable. You will work with minimal supervision within your feature area, begin to own design decisions under technical leadership, and use AI tools across the development lifecycle in a disciplined manner. AI-native development. Use AI tools across the full software development lifecycle in a disciplined manner. Take responsibility for the content of your AI-generated changes to artifacts, reviewing all changes and applying appropriate tooling and processes with minimal guidance, and apply Responsible AI practices. Coding. Create and implement extensible, maintainable code for a product, service, or feature with minimal supervision, reusing code where applicable. Apply coding patterns and best practices, including GenAI-assisted development. Escalate blockers and unknowns and communicate how they impact timelines. Code reviews. Review product features and test code to ensure it meets team standards, has correct test coverage, and is appropriate for the feature. Bring insight to code reviews to improve code quality and help develop other engineers' skills, reviewing for coding patterns, security risks, compliance issues, and best practices. Design and architecture. Provide feedback on architecture proposals under technical leadership, test and explore design options while outlining strengths and weaknesses and begin to own or collaborate on the architecture of solutions. Help ensure designs meet performance, scalability, resiliency, cost (COGS), security, and privacy requirements, and contribute to design documents that support user stories. Testing and quality. Create a clear test strategy that ensures solution quality and prevents regression with minimal supervision. Add tests to cover gaps, fix or remove broken tests, build testable code, and leverage AI tools for test automation, including security testing to validate security invariants. Customer requirements. Work with internal stakeholders (e.g., product managers, security/privacy subject matter experts, technical leads) to understand customer and user requirements for a set of features. Incorporate customer insights and continuous feedback loops and advocate for the security and privacy needs of customers. Dependencies and coordination. Identify dependencies and incorporate them into design documents, understand downstream interactions between systems, and collaborate with other teams to reach common goals where dependencies and validation concerns overlap. Planning and implementation. Execute project plans, release plans, and work items; contribute to breaking larger work items into smaller ones with estimation; escalate issues that might cause delays; and ensure security, privacy, and compliance requirements are accounted for in planning with minimal guidance. Deployment and velocity. Support safe deployment to customers by following safe-change practices, contribute to deployment automation targeting zero-touch deployment, monitor dependency status to ensure only the latest secure versions ship, and help identify when rollback plans should be enacted. Experimentation. Conduct experiments using feature flags and flighting to determine the impact of changes, collaborating with internal partners (e.g., Data Science, product managers) to incorporate success and guard rail metrics. Engineering excellence. Apply secure design principles and “security as code,” build and share tools that improve engineering efficiency, reuse open-source or internal code responsibly, and stay current with developments that...