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Salary: USD 160,000 - 240,000 / annual
AssemblyAI is seeking a Finance Engineer to build the data pipelines, dashboards, and automation that power the finance function and revenue operations. The company processes 1M+ hours of audio daily, serves 600M+ monthly inference calls, and operates as a capital-efficient AI business with ~$600K ARR per employee.
You will own the financial data infrastructure that enables the finance team to operate lean and move fast. This is a cross-functional role reporting to the VP Finance, working closely with the Controller, sales leadership, and engineering/infrastructure teams.
Key responsibilities include:
- Building and maintaining unit economics models (cost-to-serve, gross margin, contribution by product/customer/workload) from source data rather than spreadsheets
- Owning technology budget modeling: cloud and GPU/TPU spend attribution, vendor forecasting, and capacity planning models for engineering leadership
- Creating finance dashboarding as a single source of truth for burn, runway, margin, and company KPIs for leadership and board
- Automating budget-vs-actuals with live department-level views and overspend alerts
- Building deal desk framework with pricing guardrails, automated quote/order-form generation, and routing logic
- Automating sales compensation: implementing comp tools and pipelines so statements, accelerators, and disputes run from CRM and billing data
- Designing and shipping finance agents and internal tools that allow non-engineers to get answers without filing tickets
- Building and maintaining the finance data layer with pipelines from billing, payroll, cloud, and accounting systems into a modeled warehouse
- Partnering with finance and engineering leadership to trace discrepancies and ship lasting fixes
The ideal candidate is an engineer first with strong data engineering fundamentals (SQL, Python, warehouse modeling, orchestration like dbt or Airflow) combined with enough finance and operational fluency to model revenue, cost, and margin independently. You should have experience building internal tools and agent workflows that non-engineers rely on daily. You define what a number is for before building it, stay skeptical of results until they reconcile, and treat unexplained variance as a problem to solve. You're comfortable going deep across the full stack—from pipelines to billing events to cloud tags—and have a track record of automating manual processes.
Requirements:
- Strong data engineering fundamentals: SQL, Python, warehouse modeling, orchestration (dbt, Airflow, or equivalent)
- Measurement discipline: define metrics before building, reconcile results, treat variance as a problem
- Appetite for the whole stack: willingness to trace issues across billing, cloud, and operational systems
- Experience working with financial or operational data (revenue, cost, billing, payroll, cloud spend) with fluency in revenue, cost, and margin structures
- Track record building internal tools, dashboards, or automations that non-engineers rely on daily
- Familiarity with LLM-based agent workflows and clear-eyed view of where they break
- Enthusiasm for automating processes and making manual work disappear
- Strong Python skills
- Excellent communication and collaborative mindset
Bonus qualifications:
- Finance or accounting background (FP&A, analytics, accounting experience, or strong grasp of usage-based and API unit economics)
- Revenue operations exposure (CRM/CPQ data, sales comp tools, deal desk experience)
- Experience with accounting, billing, or ERP system APIs (NetSuite, Stripe, or similar)
