We're not looking for someone who uses AI tools. We're looking for someone who has moved past that - into building the systems that make AI output reliable, consistent, and self-improving.

In practice: you invest as much time improving your development system as you do shipping features. When AI output isn't right, you don't manually fix it and move on - you encode the correction as a rule, a hook, or a reusable skill so that category of problem doesn't happen again.

The Sr. Software Engineer has a sufficient understanding of the product and purpose of their team and is meaningfully involved in discussions related to feature implementation, technical design, risk, and delivery estimates. They are capable of identifying dependencies ahead or preparatory work is required to keep the team unblocked. They are able to @context switch between concurrent streams of work, e.g. major pivots in prioritization or emergent live issues, as needed. They are able to operate within a large codebase and understand its structure, overall system architecture, as well as its connection to external processes (e.g. CI/CD, automated testing, non production environments, linters, etc.). They can fluently discuss the current health of the current base particularly as it pertains to trade offs between technical debt vs new feature development as well as dependencies that may influence time estimates.

You'll work across a multi-service architecture spanning a Django/Python backend, React frontends, and a shared component library. You'll own features end-to-end - from database schema to UI component.

What you'll do:

You maintain living @context files (CLAUDE.md or equivalent) encoding your project standards and learned preferences - and update them every time you correct AI output.

You design verification systems - hooks, CI gates, acceptance criteria - so you don't need to review every AI output line by line. You build the system that verifies; you don't become the system.

You treat every repeated manual correction as a signal that your system needs improvement. Bug fixes become rules. Patterns become reusable skills. Preferences become persistent @context.

You can run multiple agents on concurrent workstreams and coordinate their output without losing coherence.

You catch what agents miss - logic errors that pass tests, subtle architectural drift, hallucinated APIs - and you improve the system rather than just patching the output.

You allocate real time to system improvement, not just feature delivery. You understand the compound effect.

Requirements

Minimum Qualifications & Credentials

Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

5+ years of professional software development experience.

Technical Skills

Proficiency in one or more back-end programming languages (e.g., Python, Java, Node.js, Go, Ruby) and one or more front-end programing languages (JavaScript - both vanilla ES6+ and React)

Experience with RESTful APIs, GraphQL, or other API architectures.

Strong knowledge of database systems (e.g., SQL, NoSQL) and query optimization techniques.

Familiarity with cloud platforms (e.g., AWS, Azure, Google Cloud) and containerization tools (e.g., Docker, Kubernetes).

Understanding of software development principles, including object-oriented programming, design patterns, and testing.

Experience with version control systems (e.g., Git).

Experience with CSS - responsive layouts, design system tokens, component-scoped styles

Actively uses AI coding assistants (Cursor, Copilot, Claude, etc.) as part of daily workflow - not occasionally, but as a default

Can evaluate AI-generated output critically, catch hallucinations, and integrate suggestions with judgment

Has used AI tools to accelerate code review, documentation, test generation, or debugging

Comfortable prompting LLMs for architecture exploration, refactoring, and boilerplate reduction

Strong understanding of software development fundamentals, including algorithms and data structures.

AI Workflow (This Part Matters Most)

Uses AI coding tools - Claude Code, Cursor, Copilot, or equivalent - as a default, not occasionally.

Has built and maintained living AI @context files (CLAUDE.md or equivalent) for a real project.

Has designed automated verification workflows (hooks, test gates, CI checks) to reduce reliance on manual AI output review.

Can demonstrate a compound system: one where AI behavior improved over time due to deliberate system investment.

Has moved beyond using AI as a reviewer-assisted assistant (writing code, then checking every output manually) into building systems that verify themselves - where rules, hooks, and @context files do the quality work so you do not have to.

Understands how agents can game tests, and has concrete strategies to detect or prevent it.

Benefits

Why You'll Love Working Here

At ConsumerAffairs, your voice matters. We foster a collaborative environment where you're encouraged to take initiative, experiment boldly, and grow professionally. We're committed to work-life harmony, career development, and celebrating wins together.

Health Care Plan (Medical, Dental & Vision)

Retirement Plan (401k)

Life Insurance (Basic, Voluntary & AD&D)

Paid Time Off (Vacation, Sick & Public Holidays)

Family Leave (Maternity, Paternity)

Short Term & Long Term Disability

Training & Development

ConsumerAffairs

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