AI-Native Software Engineer

What you will be working on

Assets Platform Development

• Design, build, and maintain backend services and APIs for managing assets, including organization structures, folders, files, and metadata, along with their create, update, and delete flows.
• Implement and evolve access control and permission features, including sharing assets with users and teams and managing visibility settings.
• Build features for secure and performant asset delivery, including CDN delivery, caching, and event driven integrations with other platform services.
• Build and enhance image editing capabilities such as cropping, resizing, rotation, format conversion, compression, and on-the-fly image transformations for delivery.
• Build and enhance video editing and processing capabilities such as trimming, transcoding, thumbnail generation, and optimized streaming and playback.
• Explore and integrate AI-powered media features such as smart cropping, background removal, auto-tagging, and automated metadata generation for images and videos.
• Build and improve asset usage tracking, so users can see where assets are referenced before changing or deleting them.
• Contribute to upcoming roadmap areas around security, collaboration, and sharing. Roles & Responsibilities

AI-Native Engineering Practice

• Use AI tools and agents as your primary development partners for designing, writing, refactoring, testing, and reviewing code, while staying fully accountable for the correctness, security, and maintainability of everything you ship.
• Break down product requirements into technical designs and implementation plans with AI assistance, and validate them against the existing architecture and constraints.
• Build and maintain reusable prompts, agent workflows, and project context so the rest of the team can adopt the same AI SDLC practices.
• Use AI agents for multi-step work such as large refactors, API changes, test generation, and bug triage.
• Measure and share the impact of AI-driven workflows on cycle time, quality, and defect rates, and continuously improve the team's practices.
• Follow company policies on data privacy and security when using AI tools, especially around customer data, secrets, and asset content.

• Own quality end to end, with no separate QA hand-off. This includes writing and maintaining automated tests (unit, integration, and end-to-end) with AI assistance, covering core flows such as upload, retrieval, editing, delivery, and permissions.
• Oversee AI-driven code reviews by setting review standards, validating AI findings, and giving final approval before changes are merged.
• Investigate and resolve production issues and customer-reported bugs, using AI to speed up log analysis, root-cause analysis, and fix verification.
• Participate in on-call rotations and incident response, and contribute to runbooks and postmortems.
• Keep technical documentation current, including API docs, design documents, and onboarding guides, using AI to draft and maintain them.

• Work closely with Product, Design, and partner engineering teams on cross-team features. Mentor other engineers on effective, responsible AI-assisted development.

Mandatory Skills

• 6+ years of professional software engineering experience building backend services and APIs. • Demonstrated, hands-on experience using AI coding tools and agents in daily work, with concrete examples of shipping production features through AI-driven workflows.
• Strong skills in prompt design, providing effective context to AI tools, and critically evaluating AI output.
• Experience with distributed systems, REST and gRPC APIs, event-driven architecture, and cloud infrastructure.
• Solid understanding of access control, data security, and multi-tenant SaaS systems.
• Strong testing discipline and debugging skills, with experience owning quality without a dedicated QA function.