Senior Product Engineer

Responsibilities

• Build systems businesses can rely on in production
• 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.
• Model how businesses actually work
• 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.
• Own problems from discovery to impact
• 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.
• Design modular systems with clear guardrails
• 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

Additional Information

Quanta 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. The Team 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. What We're Building 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.

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