Finance Engineer

AssemblyAI · United States

  • Junior
  • Full-time
  • $160,000 – $240,000
  • Posted 2026-09-24
  • Confirmed live on 25 September 2026

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Job description

Why AssemblyAI

AssemblyAI builds the best-in-class Voice AI models powering the next generation of voice applications. Our models serve 600M+ inference calls monthly, process 1M+ hours of audio daily, and power 2 billion+ end-user experiences. The Voice AI space is at an inflection point; we’re looking for folks truly excited to join a small team and help define the future of the industry.

We are one of the most capital-efficient AI companies on the planet - with under 100 people generating roughly $600K ARR per employee, we sit among the top 5 most revenue-dense teams within the fastest-growing AI companies today. That's not an accident; it's a deliberate choice to stay lean, move fast, and give every person on the team outsized ownership and impact. With thousands of customers including Granola, Fireflies, Figure AI, and CallRail, the company has real scale - processing over 2 million hours of audio daily and handling more than 1 million API calls every day. This is a rare growth-stage opportunity where the business is proven and the trajectory is steep, but the team is still small enough that your fingerprints are on everything.

If you've ever felt buried under layers of bureaucracy, starved of real ownership, or frustrated watching your work disappear into a slow-moving org, AssemblyAI is built differently. The company operates as a true meritocracy, with no heavy planning or approval processes and no gatekeeping on the tools or information you need. For anyone who genuinely cares about voice AI, not as a trend to chase, but as a technology to build, this is the place where the most interesting problems at the most interesting scale are being solved by a team small enough that you'll actually know everyone's name.

We’re committed to creating a space where our employees can bring their full selves to work and have equal opportunity to succeed. No matter your race, gender identity or expression, sexual orientation, religion, origin, ability, age, veteran status, if joining this mission speaks to you, we encourage you to apply!

About the role:

We're looking for a Finance Engineer to join our Finance team, building the data pipelines, dashboards, and automation that run the finance function and the revenue operations around it, from deal desk to sales compensation. Our goal is to operate finance as a small, senior team that runs on systems rather than headcount — and how well we do that depends on how quickly anyone here can get a trusted number on unit economics, cash burn, or technology spend and act on it. Building that capability is the heart of this role. You'll be working inside the numbers you're automating, not alongside them.The ideal candidate is an engineer first: strong data engineering fundamentals across SQL, Python, warehouse modeling, and orchestration, combined with enough finance and operational fluency to model revenue, cost, and margin without hand-holding. Experience building internal tools and agent workflows that non-engineers rely on daily is what sets this candidate apart.

This is a cross-functional role. You'll work closely with the VP Finance, our Controller, sales leadership, and engineering and infrastructure leadership — not as a handoff point, but as the person who learns enough of each domain to follow a number back to its source. At times you'll build the model, run the analysis, and present the finding yourself, both to deliver impact directly and to learn what's worth automating next. The bar is someone who understands the decision a number is meant to support, measures it from the outset, and would rather find out a model was wrong in a week than at quarter close. That discipline is what turns cross-functional ownership into an advantage.

The role is embedded within the Finance team and reports to the VP Finance.

What You’ll Do:

• Own unit economics — build and maintain cost-to-serve, gross margin, and contribution models by product, customer, and workload, rebuilt from source data rather than spreadsheets.

• Own budgeting for the technology organization: cloud and GPU/TPU spend attribution, vendor and capacity forecasting, and the models engineering leadership uses to plan.

• Build and run finance dashboarding — a single source of truth for burn, runway, margin, and company KPIs that the leadership team and board can trust.

• Automate budget-vs-actuals: live department-level views and overspend alerts that make budget and margin targets enforceable without manual chasing.

• Build the deal desk framework: pricing and discount guardrails derived from the margin model, automated quote and order-form generation, and routing so only non-standard deals need escalation — raising deal throughput without finance in the loop on every deal.

• Automate sales compensation: implement a comp tool and the pipelines behind it so statements, accelerators, and disputes run from CRM and billing data instead of spreadsheets.

• Design and ship finance agents and internal tools

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