About the company

A company building the modern data platform for accounting and finance the platform that finally delivers live, rich, trustworthy financial data and insight companies can actually run decisions on.

Its customer base numbers in the hundreds and includes many of the best-known names in AI, fintech, design, and developer tools. The company raised a $51M Series B in late 2025, backed by top-tier venture firms alongside founders and executives from category-defining fintech and infrastructure companies.

Why this work matters

Accounting is a large and growing data problem. Every business would like to ground its strategy and analysis in current, detailed information. Financial data, though, tends to be poor, fragmented, delayed, and stripped of context — and the accounting work required to assemble it is tedious and error-prone. Fixing that requires foundational change in accounting and across the broader finance function.

The company is going after the root cause: building the infrastructure and product experiences to ingest events, transform them, match value movement, project interpretations, and report against a unified graph of financial data. In short, applying the principles of software engineering and data systems to make accounting scale.

Accounting does not need more duct tape. The team has rethought the discipline its primitives, its assumptions, its practices while staying anchored to how customers actually work. The engineers here do their homework: they study the domain and talk to users, and that is what earns them the right to bring a fresh perspective to the problem.

The role

The Close product is the operating center for accounting and finance teams, weaving collaboration, automation, and workflow tools together with deeply integrated financial data and reporting. Customers rely on it to flag financial transactions, run trend analysis, match money movement across accounts, and much more as they close the books each month.

The company is looking for engineers who enjoy shipping in a tight loop with real customers and who take pride in steadily raising the quality and robustness of the systems they work in.

You might be a fit if

1. You practice "leave it better than you found it" the kind of engineer who refactors and improves the codebase, systems, and tooling as a matter of habit
2. Tight feedback loops with active customers energize you, and shipping visible change to users is the part you like
3. You reason comfortably about resilience, testing, and observability
4. You are interested in building the systems that support and manage AI-assisted development — moving faster without giving up stability or quality

Requirements

1. 3+ years of full-time software engineering experience, ideally including a stretch of more than two years at one company
2. Fluency in backend fundamentals: data structures, database internals, performance
3. Code design skills sufficient to build robust, high-quality programs

Bonus points

1. B2B SaaS experience, particularly in finance, accounting, or another complex domain
2. A strong AI development workflow
3. Strong product thinking, user empathy, or design sensibility

How the team works

This role is in-person in New York City.

Engineers here learn the domain, meet their users, and exercise real creativity and agency over product direction. The team moves fast, operates with high integrity, and genuinely likes collaborating.

The company believes the best business is the one with the best individual contributors, and has built its culture to maximize their work. Great product and engineering are seen as the output of three things: a desire to win, deep care for the craft, and pragmatism about shipping for overall impact. Engineers ground their work in a real understanding of the users, the domain, and the organization's goals.

Two notes. The customer and investor names are the single strongest credibility signal in the source, so if this isn't running blind, put them back generalizing them costs real pull. And the "ideally >2 years at one company" line reads as a tenure filter; it will screen out strong candidates from startups that got acquired or shut down, so consider softening it to something about seeing systems through over time.