• Design and build small, high-impact full-stack tools from concept to deployment
• Own the full SDLC: design, development, testing, deployment, and iteration
• Work within a small pod to deliver quickly with a high degree of autonomy
• Rapidly prototype and validate solutions with internal stakeholders
• Integrate with third-party tools, APIs, and internal systems
• Make pragmatic technical decisions appropriate for fast-moving tools
• Use AI tools to accelerate development, testing, and code quality
• Continuously refine, replace, or retire tools based on usage and feedback
• AI-Native Development
• Hands-on experience using AI-assisted development tools beyond basic code generation
• Ability to leverage AI across the workflow (e.g., prototyping, debugging, test generation, QA, code review, security analysis)
• Experience combining AI with automation/orchestration to streamline workflows and reduce manual effort
• Familiarity with modern AI-enabled development environments and practices
• Infrastructure as Code (e.g., Terraform)
• CI/CD and modern DevOps practices
• Experience with workflow automation/orchestration tools (e.g., n8n, Zapier, Temporal, Airflow)
• Experience integrating SaaS tools and APIs (Slack, Notion, Jira, etc.)
• Comfort building lightweight internal UIs (dashboards, admin panels, etc.)
• Experience with one or more of the following languages: Python, TypeScript, Golang, Rust
• Basic understanding of data engineering principles
• ## What success looks like
• You ramp quickly and start contributing within weeks
• You identify inefficiencies and ship tools to address them
• You deliver simple solutions that meaningfully improve team productivity
• You avoid overbuilding and focus on practical outcomes
• You effectively use AI tools to increase speed and output