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AI Solutions Engineer - Accounting
Отличная вакансия для AI-инженеров, ориентированных на продукт. Компания предоставляет неограниченный доступ к топовым AI-инструментам и работает над реальными задачами автоматизации в публичной компании.
Сложность вакансии
Роль требует высокого уровня самостоятельности и глубоких знаний в области системного проектирования и автоматизации. Основная сложность заключается в необходимости внедрять AI в строго регулируемую финансовую среду (SOX, аудит).
Анализ зарплаты
Зарплата не указана в вакансии, но для позиции уровня Senior AI Engineer в Торонто рыночные показатели весьма высоки. Предложение Opendoor включает опционы (equity), что типично для технологических компаний США, работающих в Канаде.
Сопроводительное письмо
I am writing to express my strong interest in the AI Solutions Engineer position at Opendoor. With over 5 years of experience in building complex automation workflows and a deep passion for agentic AI, I am excited by your mission to integrate AI into the core of financial operations. My background in Python, SQL, and system design aligns perfectly with your need for a builder who can transform messy business processes into clean, automated systems.
In my previous roles, I have consistently focused on high-agency problem solving, utilizing tools like LLMs and workflow automation platforms to drive operational efficiency. I am particularly impressed by Opendoor's commitment to providing enterprise-level AI infrastructure, such as Claude Code and Cursor. I am eager to bring my technical expertise and tinkering mindset to your Finance team to build scalable, auditable, and impact-driven AI solutions.
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Описание вакансии
Location
This role requires working from our downtown Toronto office 4 days/week (Mon, Tues, Thurs, Fri). Candidates must be based within a 50-mile commuting distance of the office.
About the Role
At Opendoor, AI isn’t a side project - it’s how we work. Across the company, teams default to AI to solve problems, ship faster, and remove friction from everything we do. You will help define the future of how we work by building AI powered workflows, automation, and experiences that drive leverage across Finance — spanning month-end close, SEC Reporting, FP&A, Technical Accounting, Tax, Procurement, Accounts Payable, and Payroll. This is high agency work in high ambiguity - turning messy problems into clean solutions, prototyping fast, and shipping.
You'll make systems design and tooling decisions that affect how our Finance operations scale – so we need people who think critically about what and why they build, the impact it will have on our team, and how to measure success.
We're looking for AI-native builders who know how to automate complex operational workflows. The accounting and finance domain knowledge can be learned — what matters most is that you're wired to build, that you default to AI in how you approach problems, and that you can map messy business processes into clean, automated systems.
Why Join Us
- Build AI automation for our Financial Operations from the ground up
- Work on cutting-edge systems directly impacting key Finance processes in a public company
- Collaborate with talented engineers and finance team members
- Access to enterprise AI tools — Claude Code, Cursor, and LLM APIs. We invest heavily in AI infrastructure so you can build and experiment.
What You'll Do
This is a hands-on individual contributor role. You will design, build, and ship automations — not just evaluate tools or write strategy decks.
- Turn complex Accounting and Finance problems into clear system designs, experiments, and implementation plans
- Build and maintain AI-powered automation that improves operational efficiency, accuracy, and scale across Finance functions
- Automate workflows (journal entries, reconciliations, flux analysis) using SQL, Python, and agentic AI
- Maintain clean, auditable data throughout automated workflows, with dashboards and controls that support both operational and compliance needs
- Partner with the internal SOX team and external auditors to ensure automated processes meet compliance standards and are well-documented
- Build and scale AI literacy across the Finance team through runbooks, documentation, best practices, and hands-on training
What You'll Bring
- At least 5+ years of work experience building automation, operational workflows, or data systems in any industry.
- Familiarity with LLM deployment, context engineering and comfortable writing SQL or Python code
- Understanding of machine learning lifecycle concepts: training, evaluation, deployment, monitoring
Systems Thinking: You understand how data flows through operational systems. You can look at a business process, identify the inputs, outputs, dependencies, and failure modes, and design automation around it.
System Design: You take complex, messy problems and reduce them to simple, durable architecture.
Communication: Articulate problems clearly to both technical and non-technical audiences. You write clean documentation, build dashboards that tell a story, and can walk a stakeholder through your design rationale.
AI Alchemy: You have high agency and a tinkering mindset. You default to experimenting, running variations with new tools and methods - LLMs, automation platforms, coding assistants until you can transmute data into high quality results. You're comfortable writing SQL code, working with APIs, and stitching tools together to ship solutions fast.
Impact Driven:You optimize for impact over novelty, choosing the right tools for the job and ensure performance, scalability and resiliency.
Bonus if you have:
- Experience in Accounting or Finance
- Comfort working in regulated or compliance-sensitive environments
Tools and Technologies
You'll work across a range of tools and we expect this list to evolve. Ideal candidates will have experience with the following tech stack:
- AI coding assistants: Claude Code, Cursor, or similar
- Workflow automation: Gumloop or similar platforms
- MCP integrations
- Agentic AI frameworks or building automation flows
- Data / ERP platforms: Snowflake, NetSuite, Ramp or similar
Note: Opendoor provides enterprise-level access to all AI tools listed above. Our engineers currently consume billions of AI tokens per month across the company and are not constrained by token limits or personal account restrictions.
Compensation
Our compensation plan consists of a base salary and Opendoor equity in addition to a comprehensive package of benefits including paid time off, paid holidays, medical/dental/vision insurance, basic life insurance, and 401(k) to eligible employees. All compensation parameters are based on experience.
#LI-DM
#LI-Onsite
At Opendoor our mission is to tilt the world in favor of homeowners and those who aim to become one. Homeownership matters. It's how people build wealth, stability, and community. It's how families put down roots, how neighborhoods strengthen, how the future gets built. We're building the modern system of homeownership giving people the freedom to buy and sell on their own terms. We’ve built an end-to-end online experience that has already helped thousands of people and we’re just getting started.
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Навыки
- Python
- SQL
- LLM
- Machine Learning
- System Design
- Snowflake
- NetSuite
- API Integration
- Automation
- Agentic AI
Возможные вопросы на собеседовании
Проверка способности кандидата структурировать хаотичные процессы.
Опишите случай, когда вы превратили сложный и запутанный бизнес-процесс в чистую автоматизированную систему. С какими трудностями вы столкнулись?
Оценка практического опыта работы с LLM и понимания их ограничений.
Как вы подходите к обеспечению точности и воспроизводимости результатов при использовании агентных AI-фреймворков в финансовых задачах?
Важно для работы в финансовом секторе (комплаенс).
Как вы проектируете автоматизированные рабочие процессы, чтобы они оставались прозрачными и проверяемыми для аудиторов?
Проверка навыков работы с данными.
Расскажите о вашем опыте работы с SQL и Snowflake для подготовки данных для AI-моделей.
Оценка умения работать с современным стеком.
Какие преимущества и недостатки вы видите в использовании инструментов вроде Gumloop или MCP-интеграций по сравнению с написанием кастомного кода на Python?
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