Quanta Search
Quanta is an AI-powered accounting and finance platform that fixes a major frustration for businesses: stale and slow financial data. We can do in seconds what takes our competitors weeks, which is why we’re trusted by many of today’s fastest growing startups.
By rebuilding accounting from the ground up, we enable business decisions to be made with timely, complete, and explainable financial information. Many of today’s fastest-growing startups, including Decagon, Braintrust, Browserbase, and Mintlify, rely on Quanta as their financial foundation.
We are a tight-knit team of 17, working fully in-person in downtown San Francisco.
Our backgrounds sit at the intersection of finance and engineering. As a group, we have built accounting systems at Affirm, ledgers at Stripe and Robinhood, and companies from the ground up. We have passed CPA exams, built finance teams from one to dozens, shipped 0→1 products, and scaled systems that support billions in transactions.
We understand our customers because we have been in their shoes. We care deeply about correctness, trust, and building systems that last. We are opinionated about the future of finance and committed to building it together.
We are defining the Operational Ledger, a new financial foundation for how businesses truly understand themselves.
If you want to work on deep, consequential systems, financial data is one of the hardest places to do it well. Accounting sits at the center of every business, yet today it is slow, opaque, and stripped of the context needed to answer real questions. We use accounting as the raw material, not the end goal. By operating directly on real customer data and real workflows, we turn messy, judgment-heavy financial data into a reliable, queryable system of record.
We are building the Operational Ledger by staying unusually close to the real work. Today, that means we do the accounting ourselves for a focused set of customers. This is not a go-to-market shortcut. It is how we learn where existing systems fail, where judgment is required, and where automation actually creates leverage.
Because we operate directly on real financial data and real workflows, our iteration loop is tight. We solve concrete automation problems that exist today, not hypothetical problems framed around what AI might do someday. The product evolves directly from what breaks, what repeats, and what slows teams down in practice.
This approach lets us build systems that are correct, explainable, and ready to support much more powerful interaction layers over time. The goal is not just faster accounting. It is to establish a financial source of truth that can support querying, modeling, and plain-language exploration with confidence.
This is how we earn the right to define a new category of financial systems.
You will build and evolve the core infrastructure that runs Quanta’s accounting and finance platform in production, including ledgers, reconciliation pipelines, and automation workflows. These systems are used daily to operate real businesses, which means behavior must be predictable and failure modes must be well understood.
The work involves engineering in high-stakes areas where mistakes are costly. You will design systems that can be reasoned about end to end and that remain stable as customers, workflows, and scale increase.
A core part of Quanta’s differentiation is how we model businesses in software. Accounting reflects how a company operates, but that reality is messy and full of exceptions, and few abstractions work across companies without careful design.
You will work closely with accountants and the engineers leading our operational data model to apply, stress-test, and evolve these abstractions in real use. This includes iterating on core primitives, object-level data models, and domain interfaces as they encounter real business structures.
Getting this right has outsized impact. These models determine what can be automated, what questions the system can answer, and how confidently finance teams can rely on the system.
You will take responsibility for problems from first signal to shipped solution. Many of these originate in our internal accounting work, where we encounter friction and breakdowns firsthand.
That proximity helps us distinguish what is inconvenient from what is structurally broken. You will solve these problems first for our own team, validate them in real workflows, and then turn the right solutions into product capabilities that scale across the entire customer base.
You will design modular systems that make customization possible without sacrificing stability. Accounting workflows vary by company, and our platform must support those differences while enforcing clear guardrails around data integrity and system behavior.
Those guardrails unlock more than correctness. They enable user-facing AI products that let customers ask questions in plain language and explore insights with confidence, while also supporting accountants customizing workflows and finance leaders running projections, hypotheticals, and models. Your work will define the abstractions that allow flexibility and control to coexist at scale.
AI is moving quickly, and we see it as a chance to rethink how software gets built as a team. You will work alongside other engineers to explore how new tools, workflows, and agentic systems can change how we design, implement, and maintain our product.
This is collaborative work. Engineers build on each other’s experiments, share patterns that work, and evolve a common approach rather than everyone reinventing their own. Our constraints are concrete, which gives this exploration real direction and accountability.
You will ship quickly without cutting corners. Within your first days, you will contribute to production systems and real design decisions. Within weeks, you will ship features used by customers. Over time, your work will compound as systems become more automated, resilient, and powerful.
This role rewards engineers who want ownership, depth, and the chance to build systems that endure.
6–7+ years of full-stack engineering experience, ideally on small, high-impact B2B teams
Strong technical fundamentals across modern backend and frontend systems
Systems thinking, including API design, data modeling, and reliability
Product mindset with good judgment around tradeoffs
Comfort with ambiguity, ownership, and self-direction
Strong collaboration skills
Bonus points for experience with financial systems, ledgers, or accounting workflows.
You are excited about working in person with a collaborative team in San Francisco.
You prefer clearly defined problems with detailed specs, rather than shaping solutions in ambiguous spaces.
You are uncomfortable working in a complex, judgment-heavy domain where correctness depends on context, not just code.
You optimize for speed by accepting known gaps in reliability. Trust matters deeply in the systems we build.
You are primarily interested in AI as a standalone feature, rather than as part of a larger, constrained system.
You prefer working independently or remotely from domain experts. This team works closely and in person by design.
You have deep familiarity with Quanta’s core systems and are comfortable working where correctness is most critical.
You have owned and shipped improvements used daily by internal teams and customers.
You have shaped at least one core abstraction that increases flexibility without compromising trust.
You are a go-to collaborator for engineers and accountants, translating real-world finance problems into clear technical approaches.
You have helped define how the team uses AI in practice to increase leverage.
You are contributing to longer-term technical direction for the Operational Ledger.
You feel strong ownership over outcomes, not just code.
Competitive early-stage salary ($205K-265K)
Competitive early-stage equity
Health, dental, and vision insurance
401(k)
Commuter benefits
Wellness stipend
Take-what-you-need paid vacation
Paid lunches and dinners in the office
All equipment and tools you need to be productive
Quanta Search
Quanta Search
Windranger
Alexander Chapman