Principal/Staff AI-Native Software Engineer

Role Overview

We are looking for an exceptional Principal or Staff-level engineer who has fundamentally changed how they build software because of AI. This is not a traditional software engineering role with an AI assistant bolted on. We want someone who uses AI agents, especially Claude Code, to independently take well-defined product initiatives from business requirements to secure, tested, production-ready software.

The ideal candidate is an excellent backend engineer first, grounded in our stack: TypeScript services, MongoDB and relational data, and cloud infrastructure across AWS and GCP. AI dramatically increases your output, but your engineering judgment is what makes that output valuable.

In this role, you will work closely with our Product and Design teams. You will operate with unusually broad engineering ownership. You may clarify requirements with Product, develop the technical specification, work through implementation details with Design, coordinate multiple coding agents, review their work, deploy the application, manage its infrastructure, and continue improving the product after launch.

The ideal candidate is already working this way. You routinely operate several Claude Code sessions or agents in parallel. You know how to divide work across agents, provide them with the right context, establish effective feedback loops, and keep multiple workstreams moving without losing control of quality or architecture.

You believe manually writing code should become a relatively rare part of software development. But you are not impressed by code merely because an AI generated it. You recognize over-engineered "AI slop," unnecessary abstractions, sprawling diffs, weak tests, insecure implementations, and solutions that are technically functional but fundamentally poor.

Responsibilities

Partner with Product and Design

• Work closely with Product to understand business requirements, intended user outcomes, priorities, constraints, and acceptance criteria.
• Translate Product's business requirements into clear technical requirements, implementation plans, system behaviors, and engineering tasks.
• Identify technical ambiguities, dependencies, risks, edge cases, and tradeoffs that must be resolved before or during implementation.
• Ask the questions necessary to ensure the engineering solution addresses the actual product need.
• Partner with Design to understand intended user workflows and translate designs into complete, functional product experiences.
• Provide Product and Design with clear feedback when requirements or designs introduce unnecessary complexity, technical risk, security concerns, or operational challenges.
• Preserve the intent of the product requirements while making sound technical and implementation decisions.
• Communicate progress, tradeoffs, risks, and changes in scope clearly throughout development.

Build and Own Products End to End

• Take approved product initiatives from business requirements through technical design, implementation, deployment, and ongoing operation.
• Manage several products or major engineering initiatives with limited day-to-day oversight.
• Develop complete technical requirements from the business and product requirements supplied by Product.
• Make thoughtful architectural, implementation, security, infrastructure, and operational tradeoffs within an established platform direction.
• Design, build, and maintain efficient, reusable, reliable, and testable code; identify bottlenecks and bugs, devise solutions, and propose optimizations.
• Deliver working software quickly while maintaining high standards for reliability, maintainability, security, and user experience.
• Own the technical execution of a product rather than waiting for every implementation detail to be specified.
• Continue improving products after launch based on feedback, usage, defects, and changing requirements.

Operate an AI-Native Development Environment

• Use Claude Code as a primary development environment and orchestration layer.
• Run multiple agents, sub-agents, worktrees, branches, or development sessions in parallel.
• Decompose technical requirements into work that can be executed safely and effectively by different agents.
• Design reusable Claude Code skills, commands, hooks, context files, agent instructions, and development workflows.
• Provide agents with the technical requirements, architectural context, constraints, designs, examples, and verification methods necessary to produce high-quality work.
• Build feedback loops in which agents review, test, critique, and improve one another's output.
• Use agents to investigate unfamiliar systems, understand legacy code, create implementation plans, write code, generate tests, review changes, prepare pull requests, and assist with releases.
• Continuously improve the systems, instructions, and tooling that make your own AI-assisted development more reliable and scalable.

Automate the Product Development Lifecycle

• Help automate the engineering portions of the product development lifecycle, including:
• Translating business requirements into technical requirements
• Technical discovery and architecture
• Implementation planning
• Design and UX implementation
• Software development
• Automated testing
• Security analysis
• Code review
• Pull-request creation and iteration
• Infrastructure provisioning
• Deployment
• Monitoring and operational support
• Documentation and maintenance
• Create workflows in which agents can execute substantial portions of the development lifecycle while preserving appropriate human judgment and control.
• Establish clear quality gates so that agent-generated work is verified rather than accepted on faith.
• Develop methods for measuring the effectiveness, quality, cost, and speed of AI-native engineering workflows.
• Reduce the manual coordination and repetitive work required to move from an approved product requirement to deployed software.

Maintain Exceptional Engineering Quality

• Review agent-generated code with the judgment expected of a Principal or Staff engineer.
• Reject unnecessary complexity, excessive abstraction, inappropriate frameworks, duplicated logic, and changes that are much larger than the problem requires.
• Prefer focused, understandable solutions: for example, an eight-file targeted change rather than an eighty-file rewrite when both accomplish the same result.
• Ensure software is appropriately tested, observable, secure, maintainable, and production-ready.
• Understand how systems work beneath the abstraction layer rather than treating AI output as a black box.
• Debug complex issues across application code, infrastructure, data systems, third-party services, and development tooling.
• Know when the AI is wrong, when its proposed approach is risky, and when direct engineering intervention is necessary.
• Ensure implementations satisfy the agreed product requirements and acceptance criteria without adding unnecessary scope.

Work Across Our Stack and Beyond

• Bring deep, hands-on expertise in our core stack — Ruby on Rails, production TypeScript/Node.js, MongoDB and relational data, and cloud infrastructure across AWS and GCP.
• Use Claude Code to move confidently into adjacent or unfamiliar codebases — Android, iOS, Go, and others — developing enough understanding to make sound architectural and implementation decisions without waiting for a specialist.
• Work across frontend, backend, mobile, infrastructure, integrations, and internal tooling as needed.
• Manage infrastructure and deployment workflows across AWS and GCP.
• Improve legacy systems rather than avoiding them or insisting that everything be rewritten.

Requirements

• 8+ years of professional software development experience.
• A track record of independently delivering complex, production software in partnership with Product and Design teams.
• Expert-level experience with Claude Code. This is a mandatory requirement. You must be able to demonstrate substantial product development with Claude Code — not merely autocomplete, isolated code generation, or simple prompting.
• Experience coordinating multiple AI agents or development sessions in parallel, with a deep understanding of agent context, skills, sub-agents, tool use, planning, verification, and orchestration.
• Production experience with both a dynamic language ecosystem and a strongly-typed one. Production TypeScript/Node.js experience is required.
• Experience with relational databases (PostgreSQL, MySQL) and document/NoSQL databases. MongoDB experience is required.
• Hands-on experience deploying and operating production software in a distributed, cloud-based environment (e.g., AWS EC2/ECS, Lambda, Cloud Run) within a service-oriented or micro-service architecture, across AWS, GCP, or both.
• Hands-on experience applying AI/ML in production. For example integrating LLMs or other models into products or workflows, building retrieval or agentic systems, prompt/eval design, or on-device/edge inference.
• Extensive experience developing and maintaining APIs and integrating with third-party APIs.
• Strong experience with automated CI/CD pipelines and a deep understanding of testing what, when, and how to test.
• Strong ability to translate business and product requirements into technical requirements and implementation plans.
• Strong product judgment, including the ability to understand user intent, identify edge cases, and recognize when an implementation does not satisfy the underlying requirement.
• Strong understanding of software design, architecture, security, reliability, scalability, and maintainability.
• Ability to review AI-generated code critically and distinguish high-quality engineering from superficially functional output.
• Comfort working across unfamiliar programming languages, frameworks, and legacy systems, using AI to accelerate that work.
• Strong ownership, initiative, and bias toward building.
• Excellent written and verbal communication, particularly when expressing technical requirements, constraints, implementation plans, and engineering decisions to both business and technical audiences.