Monstro provides the AI-powered Financial Intelligence System for regulated financial institutions. It enables banks and wealth managers to deliver continuous, personalized, governed Financial Guidance to every client, not just the top tier, across their entire financial life: saving, borrowing, investing, planning, protecting, and preparing.
Meaningful Financial Guidance has only ever been available to a small portion of people. We are changing that, through the institutions people already trust. Institutions already have the relationships, the data, and the context to understand each client; Monstro turns that understanding into guidance that helps each person understand, decide, and act. For clients, that means clearer decisions, proactive support when life changes, and a better view of their financial lives. For institutions, it means deeper relationships, more clients served consistently, and more of each client's financial life managed in one place.
What makes this possible is how the system is built. Governance is not an added feature; it is foundational. Every recommendation reflects the institution's own views, policies, and permissions. Every output can be audited, traced, and reversed, and client data stays under the institution's control. Generative AI is used to build and configure the system; at runtime, execution is deterministic, so every output can be explained and stood behind, not just trusted.
The work
We are building for a high-stakes industry where the work has real consequences. Guidance only matters if people can trust it and institutions can govern it. The challenge is to make Financial Guidance more personal and more available without making it less trusted: to build AI that institutions can audit, explain, and reverse, in a domain where being wrong carries real cost. That is what makes the problem hard, and the work meaningful. This is not a simple AI problem; it takes product judgment, technical depth, regulatory discipline, design quality, and a team that moves quickly without lowering the bar.
Monstro is moving from vision to market: launching with financial institutions, growing the team, and building for the enterprise in one of the most regulated industries. You will be expected to think clearly, communicate directly, move with urgency, hold a high standard, and take responsibility for work that matters. We are an early team building something ambitious, and what you do here will shape both the product and the company.
We're looking for a Staff Software Engineer to support our AI application development team at the heart of the company's mission. This is a deeply technical role with real scope: you'll architect and ship systems that power AI-driven financial analysis, build the tooling and infrastructure that domain experts rely on, and set the engineering bar for how we build AI systems responsibly in a regulated industry.
You'll influence how we design agents, how we validate and govern AI output, how we scale our modeling infrastructure, and how we think about auditability and compliance. This isn't a role where AI is a thin wrapper — the intelligence layer is the product.
Base Compensation Range (New York City): $224,000 - $264,000
Final compensation will depend on a variety of factors, including experience, skills, internal leveling, and market conditions, and will be offered within the stated range in accordance with applicable pay transparency laws.
A Note on Interviewing: We sometimes use AI note-takers to help us transcribe interview notes, so we can be more present in your interview. If you'd like to opt out of us using automatic transcribers, please note this in the free text field in your application; otherwise we'll take your application as confirmation that you're happy for us to use note-takers (whether added to video calls or in the background).
The standard is simple: do great work, own the outcome, communicate with purpose, move with urgency, raise the level. If that sounds like you, we'd love to hear from you.
| Location | New York, NY |
| Salary | $224,000–$264,000 Per Year |
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