Software Engineer

Salary Range: 164000 to 190000 (Currency: USD) (Pay period: per-year-salary)

About the Team

The Engineering organization includes approximately 25 engineers, split between two groups: Transaction/Brokerage Management and Platform Engineering. We value diverse perspectives, backgrounds, and ways of thinking. The Transaction Management team builds the systems that power our primary agent-facing application: The Side App.

About the Role

As a Mid-Level Frontend Software Engineer on the TXM team, you will build and maintain frontend applications that support real estate transactions for our partners. You will partner closely with Product, Design, Customer Success, Brokerage Operations, and other engineering teams to deliver reliable, scalable solutions.

This role is ideal for an engineer who can independently deliver well-scoped features, contribute to system design discussions, write high-quality code and tests, and collaborate effectively across functions. You should be comfortable using modern engineering tools, including AI-assisted development tools, to improve productivity while maintaining strong judgment, code quality, and security standards.

This is a hybrid role based in San Francisco, CA. Candidates should be located in the San Francisco Bay Area and able to work from the office at least two days per week.

What you'll be doing:

Build, maintain, and improve frontend features and UI components that support real estate transaction workflows for our partners.

Collaborate with Product and cross-functional partners to turn business requirements into technical solutions.

Partner with design stakeholders to deliver pixel-perfect UI from Figma files and contribute to our shared design system.

Deliver medium-sized features independently, with support from senior engineers on larger or more complex initiatives.

Participate in system design discussions and contribute practical ideas around scalability, performance, reliability, and maintainability.

Write clean, well-documented, and well-tested code.

Create and maintain automated tests across unit, integration, and end-to-end layers.

Review code thoughtfully and incorporate feedback from others.

Troubleshoot production issues, debug complex frontend bugs and UI regressions, and support operational excellence.

Identify technical debt and suggest pragmatic improvements.

Follow engineering best practices for security, observability, data integrity, and application performance.

Leverage AI-assisted engineering tools to improve productivity across prototyping, debugging, task management, documentation, and code review, while applying strong judgment to validate correctness, security, and maintainability.

What would make you a strong fit for this role?

3–5 years of experience building production frontend applications.

Strong proficiency in TypeScript and React.

Solid experience with Next.js and modern React patterns (hooks, server/client components, App Router).

Intermediate proficiency in CSS and its core concepts (box model, Flexbox, CSS Grid, positioning, specificity, media queries, etc.)

Comfort working in a large monorepo with shared libraries and internal conventions (Nx, Yarn workspaces).

Strong experience with testing using Jest, React Testing Library, and Cypress.

Experience working with Git and GitHub in a collaborative development workflow.

Ability to break down technical problems and deliver maintainable solutions with guidance when needed.

Strong communication skills and the ability to work effectively across engineering and non-engineering teams.

Good judgment around reliability, security, and performance in production systems.

Comfortable learning and adapting to new tools and workflows, including AI-assisted development tools.

Nice to Have

Experience with design systems or component library work (e.g., MUI, Radix, or similar).

Experience with observability and production monitoring tools.

Familiarity with cloud platforms and modern CI/CD practices.

Experience contributing to open-source projects.

Familiarity with building internal tools or automations using LLMs, agents, or workflow-based AI systems.