AssemblyAI

AssemblyAI

Finance Engineer

United States

Sponsorship not specified$160k-$240kDetected 22 days ago
PythonSQLAirflowdbtData EngineeringLLMsComplianceHubSpotSalesforceCRMAccountingForecastingBudgetingFP&AERPLeadershipCommunicationNetSuite

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odds of building a lasting career here

53Sponsors, lottery-bound
Cap-exempt (no lottery)0
Sponsors this role60
Entry-level history0
PERM / green-card track0
Lottery odds (Level IV)94
Fits your clock70

Sponsors, but it's cap-subject — you still face the weighted lottery (~61% per draw at Level IV). Good if you win; have a cap-exempt backup on your list.

Lottery odds assume a STEM candidate.

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About the role

  • 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.
  • 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.
  • That discipline is what turns cross-functional ownership into an advantage.

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

Requirements

  • 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.
  • Familiarity with LLM-based agent workflows and comfort applying them to real operational processes, with a clear-eyed view of where they break.
  • Not required - the VP Finance owns the finance judgment
  • 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.
  • Strong Python skills; experience with accounting, billing, or ERP system APIs (NetSuite, Stripe, or similar) is a plus.
  • 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.

Nice to have

  • Strong Python skills
  • experience with accounting, billing, or ERP system APIs (NetSuite, Stripe, or similar) is a plus.

Skills

  • Why AssemblyAI
  • Our models serve 600M+ inference calls monthly, process 1M+ hours of audio daily, and power 2 billion+ end-user experiences.

Compensation

  • $160,000 - $240,000
  • 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.
  • 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.

Benefits

  • AssemblyAI strives to recruit and retain exceptional talent from diverse backgrounds while ensuring pay equity for our team.
  • 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.

Company info

  • 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.
  • 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.
  • 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.
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This listing is sourced directly from AssemblyAI's careers page and normalized into a canonical job model.