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AI Enablement Engineer
Привлекательная позиция в стабильном стартапе стадии Series C с серьезными инвесторами (a16z). Высокая автономность и работа с передовым стеком технологий делают эту роль отличным шансом для инженеров, любящих быстрый темп.
Сложность вакансии
Роль требует высокой скорости разработки и умения работать в условиях неопределенности без детальных спецификаций. Необходимо владеть полным стеком (React/TypeScript/Next.js) и активно использовать AI-инструменты в процессе написания кода.
Анализ зарплаты
Зарплата в объявлении не указана, но для позиции инженера с опытом 1-5 лет в Нью-Йорке в компании стадии Series C рыночные показатели обычно выше среднего. Учитывая специфику AI и высокую планку по скорости, можно ожидать конкурентоспособное предложение, дополненное опционами.
Сопроводительное письмо
I am writing to express my strong interest in the AI Enablement Engineer position at Valon. With a background in full-stack development and a passion for building high-velocity internal tools, I am particularly drawn to Valon's mission of transforming the mortgage servicing industry through an AI-native operating system. My experience in shipping end-to-end products and my fluency with AI-assisted development tools like Claude and Codex align perfectly with your team's focus on rapid deployment and high autonomy.
In my previous projects, I have consistently demonstrated the ability to turn ambiguous requirements into polished, functional software. Whether it's building complex Kanban boards or integrating RAG systems to automate data extraction, I thrive in environments where I can own the full stack. I am excited by the prospect of joining a Series C company backed by a16z and contributing to the development of ValonOS, ensuring that client deployments are as efficient and automated as possible.
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Описание вакансии
About the Company
Valon is building the AI-native operating system for regulated finance, starting with mortgage servicing.
We're a Series C company backed by a16z, transforming industries that others have written off as too complex to innovate.
Rather than build on top of broken legacy systems, we took a different approach: we built and operate our own mortgage servicing business managing $110+ billion in loans. This wasn't the end goal, it was how we deeply understood the complexity needed to build software that actually works in regulated industries.
The results speak for themselves. We've transformed mortgage servicing from a 0% margin business into 60%+ margins while dramatically improving customer experience. Major enterprise contracts are now deploying across the industry.
ValonOS is our unified platform that makes every process structured and programmable and it is perfectly positioned for the AI era. When everything flows through one system with rich data, AI agents don't just automate tasks, they continuously improve entire operations. Mortgage servicing is just the beginning of our vision to transform regulated industries and beyond.
About the Role
We're hiring an AI Deployment Engineer to join a small, high-autonomy team building the internal tools and AI-powered systems that make Valon's client deployments successful.
This is not a traditional software engineering role. You'll operate at the intersection of product, engineering, and client delivery — building whatever is needed to make deployments run smoothly. One week you're standing up a project tracking dashboard; the next you're integrating AI agents into client workflows; the week after you're building admin tooling so deployment leads can manage teams and forecasts.
To give you a sense of pace and scope: building a full deployment management platform — kanban boards, Gantt views, milestone tracking, AI-powered inbox scanning, team dashboards — in six weeks is the kind of output we expect from this role.
What You'll Do
- Build deployment tooling end-to-end — Design, build, and ship internal applications that help Valon manage complex client deployments. You'll own the full stack from data layer to UI.
- Ship AI-powered features — Build AI agent workflows, copilot experiences, and automation tools that make deployment teams more effective. Think: intelligent inbox scanning, cross-reference extraction, AI-assisted planning.
- Move fast with AI-assisted development — Use coding agents (Claude Code, Codex) as pair programmers to maintain high velocity. We expect you to ship meaningful features daily, not weekly.
- Work across the stack — Next.js, React, TypeScript, Tailwind CSS, API integrations. You don't need to know all of these coming in, but you need to learn fast and build confidently across layers.
- Operate with high autonomy — You'll have broad ownership over what you build and how. No one will hand you detailed specs — you'll identify problems, design solutions, and ship them.
Who You Are
- 1–5 years of engineering experience, with demonstrated ability to ship complete products (not just features within a larger codebase)
- Full-stack web development chops — strong in React/TypeScript; comfortable building APIs, managing data layers, and crafting polished UIs
- AI-fluent — you actively use coding agents in your development workflow and have built AI-powered features (agent workflows, RAG systems, copilot UX, or similar)
- Extremely high velocity — you're the person who builds a working prototype while others are still writing the design doc
- Comfortable with ambiguity — you thrive when given a problem, not a solution, and can turn vague requirements into shipped software
- Strong product instincts — you build things people actually want to use, with attention to UX details like drag-and-drop, inline editing, and responsive layouts
Nice to Have
- Experience at early-stage startups or as a technical founder
- Forward-deployed engineering background (Palantir, Anduril, Scale AI, or similar)
- Familiarity with project management / deployment tracking domains
- Experience building on Airtable, Notion, or similar low-code backends
- Contributions to AI tooling or developer tools
What Won't Work
- Engineers who need detailed specs and defined scope before they can start building
- Candidates who are strong backend but uncomfortable building polished frontends
- People who talk about AI but haven't built with it
- Anyone uncomfortable working solo with high ownership and minimal oversight
Benefits
- Competitive salary + equity (Series C stage)
- Comprehensive health, dental, and vision insurance
- Commuter benefits
- Flexible PTO
- 12 weeks paid parental leave
- Quarterly team events
Throughout the interview process, please remember that emails will only be from valon.com email addresses. We will never ask for any personally identifiable information during the interview process itself. Please reach out to talent@valon.com if you have any requests to verify the authenticity of an outreach.
Valon is an equal opportunity employer that is committed to diversity and inclusion in the workplace. We prohibit discrimination and harassment of any kind based on race, color, sex, religion, sexual orientation, national origin, disability, genetic information, pregnancy, or any other protected characteristic as outlined by federal, state, or local laws. Valon makes hiring decisions based solely on qualifications, merit, and business needs at the time.
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Навыки
- TypeScript
- LLM
- API Integration
- RAG
- React
- Node.js
- Next.js
- Tailwind CSS
- Claude
Возможные вопросы на собеседовании
Проверка способности кандидата быстро создавать работающие прототипы и принимать архитектурные решения на лету.
Опишите случай, когда вам нужно было собрать сложный продукт с нуля за очень короткий срок (например, пару недель). Какие компромиссы вам пришлось пойти?
Вакансия требует 'AI-fluent' кандидата, который не просто слышал об AI, но и внедрял его.
Расскажите о самом сложном AI-агенте или функции на базе LLM, которую вы разработали. С какими проблемами точности или интеграции вы столкнулись?
Роль предполагает создание инструментов для внутренних команд без четких ТЗ.
Как вы подходите к сбору требований, когда заказчик (например, отдел внедрения) дает лишь общее описание проблемы без конкретных макетов?
Вакансия подчеркивает использование AI-ассистентов (Claude Code, Codex) для ускорения работы.
Как именно вы используете AI-инструменты в своем ежедневном рабочем процессе программирования? Как это изменило вашу производительность?
Важно понимание продуктовой составляющей, а не только написание кода.
Какие элементы UX вы считаете критически важными для внутренних инструментов управления проектами, чтобы ими действительно хотели пользоваться?
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