Principal Frontend Engineer, AI Product UX Location: San Francisco, CA Department: Engineering Job Description: Location San Francisco Employment Type Full time Location Type Hybrid Department Engineering Principal Frontend Engineer, AI Product UX Location: San Francisco — hybrid, in-office three days a week Compensation: Competitive base + leadership level equity Help define how humans trust AI decisions AI is transforming mortgage lending, but underwriting decisions still require humans to verify, interpret, question, and approve what AI systems produce. The challenge is not just delivering automation. It is designing interfaces that help professionals confidently work alongside AI in high-stakes financial workflows. That’s the problem we’re solving. HomeVision is the AI underwriting platform for the $13 trillion U.S. mortgage industry. Our platform, MIRA, has already automated more than two million appraisal reviews for many of the country’s largest lenders and appraisal management companies, reducing review time by 75% and doubling underwriter productivity. Now we’re expanding beyond appraisal review into the full underwriting workflow, including income, assets, credit, collateral, and loan file decisioning, to create the first end-to-end AI underwriting utility for the industry. We’re doing this alongside real enterprise customers with contracted production demand, including Newrez, the fourth-largest mortgage lender in the U.S. and a strategic investor in the company. We’re also backed by Initialized Capital and NVP Capital. We’ve been building AI systems in this space well before “AI company” became a trendy label. The recent acceleration of agentic systems and AI tooling has only expanded the scale of what’s now possible. We’re looking for a principal-level frontend/product engineer to help define the interaction layer between humans and AI throughout the underwriting process. You will partner directly with our Head of Design, our CTO, and senior product leadership to shape the core interaction model of the platform, not just implement them. What you’ll do - Define the future of AI-assisted underwriting. Design and build workflows that help underwriters, processors, and loan officers confidently work alongside AI systems and build trust. Create intuitive UX patterns for explainability, auditability, exception handling, and human-in-the-loop review. - Own the cross-platform frontend and design system. Lead the evolution of our frontend platform across web and future embedded experiences, including in-document review and lender integrations. Architect components so the team can ship faster as we scale. - Raise the technical bar. Establish the frontend patterns, libraries, and review standards the rest of the engineering team builds against. Mentor and recruit the engineers who will work alongside you. - Ship fast and learn faster. Take features from rough ideas to production in days, not weeks. Watch how customers actually use them and rapidly iterate. - Drive tools and process improvement. Share ideas and test tools that accelerate prototyping to production at higher velocity and quality. Who you are - You’re deeply product-oriented. You enjoy engaging with customers and learning a new domain. You think beyond implementation details and care how technical decisions shape user behavior and product outcomes. - You’re a builder at heart. You’ve shipped meaningful products used by real customers. You likely have side projects, strong opinions about technology, and genuine curiosity about where AI is heading. You are a creator. - You operate with urgency and ownership. You move quickly without sacrificing reliability or trust. You proactively identify problems, communicate clearly, and drive solutions. - You elevate the people around you. You bring strong engineering judgment, high standards, and collaborative energy. Teams become stronger when you’re involved. - You’re excited by difficult workflow problems. Complex systems, making sense of dense information and documents, operational tooling, and AI-assisted decision-making genuinely interest you. What you’ll bring on the technical side - Deep proficiency in React and TypeScript, and a clear sense of how to architect front-end systems that can extend and scale. - Comfort working across the stack when it matters — you can read and reason about our Go/Python backend even if you don’t live there. - Experience building AI-native interfaces or complex, data-dense applications. Bonus if you’ve worked on document review, decisioning, or workflow products. - Track record of leading a product surface end-to-end — not just features. How we work We’re a hybrid team headquartered in San Francisco, with team concentrations in Seattle and Argentina, with other great team members in parts of the U.S. Our values are the ones we actually use: - Start with questions, not answers. - Run at problems — make your footprint bigger than your foot. - Continuous learning, continuous improvement. - Be transparent and collaborative. - Value time. - Play to win, win for everyone. - Always do the loving thing. We’re growing the engineering team meaningfully over the next twelve months and this role will have a direct hand in shaping who that team becomes. Compensation and benefits - Competitive compensation calibrated to senior/principal-level engineers in San Francisco - Meaningful equity ownership - Full medical, dental, and vision coverage - 401(k) with company match - Flexible vacation policy - Parental leave - Learning and professional development support

• Rapidly prototype AI agents and guided workflows that solve concrete customer problems; ship thin slices, instrument, and iterate with real usage. • Design and implement cloud-native backend services that orchestrate prompts, tools, retrieval, and automations; own the path from POC to production. • Build robust retrieval for LLMs (search/vector pipelines, context assembly, tool selection) in partnership with data engineering. • Establish evaluation loops: offline/online tests, golden sets, regression checks, and human-in-the-loop review to measurably improve quality and safety. • Contribute to developer experience: reusable agents/tools, SDKs, templates, and internal documentation that speed up future builds. • Collaborate with customers and internal stakeholders to frame problems, define success metrics, and translate insights into productizable solutions. • Lead design discussions and code reviews; mentor teammates and raise the quality bar while keeping momentum high.

• Rapidly prototype AI agents and guided workflows that solve concrete customer problems; ship thin slices, instrument, and iterate with real usage. • Design and implement cloud-native backend services that orchestrate prompts, tools, retrieval, and automations; own the path from POC to production. • Build robust retrieval for LLMs (search/vector pipelines, context assembly, tool selection) in partnership with data engineering. • Establish evaluation loops: offline/online tests, golden sets, regression checks, and human-in-the-loop review to measurably improve quality and safety. • Contribute to developer experience: reusable agents/tools, SDKs, templates, and internal documentation that speed up future builds. • Collaborate with customers and internal stakeholders to frame problems, define success metrics, and translate insights into productizable solutions. • Lead design discussions and code reviews; mentor teammates and raise the quality bar while keeping momentum high.

• Work with product, engineering, and teams to cultivate a shared vision for the problem space, constraints, priorities and ideal end state, and be able to articulate and advocate for this perspective. • Lead the full lifecycle of ML and LLM platform workstreams - from data ingestion, training, and deployment to the ongoing optimization of live user workflows based on performance metrics and feedback. • Work with internal teams and end users to develop a deep understanding of requirements, perform thoughtful solution design, use data to test hypotheses, and support teams throughout execution. • Write detailed user stories for new features, capturing detailed descriptions of business rationale, requirements, and success criteria that are defined by measurable outcomes. • Develop short and long term roadmaps that deliver maximum value with minimum risk and assume ongoing iteration. • Partner with AI Researchers to mature proofs of concept into production ready capabilities. Perform hands-on exploration of user feedback, healthcare records and validation data to validate model performance.