Why AssemblyAI AssemblyAI builds the best-in-class Voice AI models powering the next generation of voice applications. We have a lab, and we also power our own models in our own proprietary inference environment. Our models serve 500M+ inference calls monthly, and process nearly 2 million hours of audio daily. The Voice AI space is at an inflection point; we’re looking for the best talent we can find to join a rapidly scaling team that's defining the future of the industry. We are one of the most capital-efficient AI companies on the planet - and are rapidly approaching $1M ARR per employee. We sit among the top 5 most revenue-dense teams within the fastest-growing AI companies today. We have thousands of customers building with us including the top startups and world-renowned enterprises, and are serving at tremendous scale: we process nearly 2 million hours of audio daily and handle more than 15 million API calls every day. This is a rare growth-stage opportunity where the business is proven, the revenue is durable, and the growth trajectory is steep and getting steeper. We have a super flat culture and expect our employees to be deeply intimate with the details at all levels. 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. 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. Responsibilities 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 — workflows that pull, reconcile, and summarize financial data so non-engineers get answers without filing a ticket. Build and maintain the finance data layer: pipelines from billing, payroll, cloud, and accounting systems into a well-modeled warehouse. Partner with the VP Finance, Controller, and engineering leadership to trace discrepancies to their real source and ship fixes that hold. Requirements Strong data engineering fundamentals, including SQL, Python, warehouse modeling, and orchestration (dbt, Airflow, or equivalent). Measurement discipline. You define what a number is for before you build it, you stay skeptical of your own results until they reconcile, and you treat an unexplained variance as a problem rather than noise. Appetite for the whole stack. Your core strength might be pipelines, but when a margin number looks wrong and traces back to a billing event or a cloud tag, you want to go find it yourself. The people who do well here went deep in one area first, then kept expanding outward. Experience working with financial or operational data — revenue, cost, billing, payroll, or cloud spend — and enough fluency in revenue, cost, and margin structures to model them without hand-holding. A track record of building internal tools, dashboards, or automations that non-engineers rely on daily. Familiarity with LLM-based agent workflows and comfort applying them to real operational processes, with a clear-eyed view of where they break. Enthusiasm for automating existing processes — you thrive on making manual work disappear and making systems faster and more reliable. Strong Python skills; experience with accounting, billing, or ERP system APIs (NetSuite, Stripe, or similar) is a plus. Excellent communication and a collaborative mindset — you can explain what a number means, and what it doesn't, to a VP Finance, a Controller, and an engineering lead. Nice to have Finance or accounting background: prior FP&A, analytics, or accounting experience, or a strong grasp of usage-based and API unit economics. Not required — the VP Finance owns the finance judgment; this seat owns the build. Revenue operations exposure: experience with CRM or CPQ data (Salesforce, HubSpot, or similar), sales comp tools (CaptivateIQ, Spiff, QuotaPath, or similar), or running a deal desk. Pay Transparency AssemblyAI strives to recruit and retain exceptional talent from diverse backgrounds while ensuring pay equity for our team. Our salary ranges are based on paying competitively for our size, stage, and industry, and are one part of many compensation, benefit, and other reward opportunities we provide. There are many factors that go into salary determinations, including relevant experience, skill level, qualifications assessed during the interview process, and maintaining internal equity with peers on the team. The range shared below is a general expectation for the function as posted, but we are also open to considering candidates who may be more or less experienced than outlined in the job description. In this case, we will communicate any updates in the expected salary range. The provided range is the expected salary for candidates in the U.S. Outside of those regions, there may be a change in the range which will be communicated to candidates throughout the interview process. Salary range: $160,000 - $240,000 Using AI in your interview If you’re selected for an interview, please read our Candidate AI Guidance. It explains how you can use AI at each stage of our process, from your application and take-home to the interviews themselves. GDPR privacy notice If you’re applying from the EU or UK, please review our Job Applicant Privacy Notice before applying. Keep exploring AssemblyAI Transcribe: Speech-to-Text | Streaming Speech-to-Text Understand: Speech Understanding | LLM Gateway Interact: Voice Agent API | Dictation API Try our models in the Playground See how 50+ LLMs rank on real voice tasks: Voice Standard Bench Watch us on YouTube
