AI Solutions Engineer - Accounting
OpendoorOntario; TorontoPosted 19 March 2026
Job Description
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, FPA, 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 Accountin ... (truncated, view full listing at source)
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