AssemblyAI
Finance Engineer
About this role
AssemblyAI seeks a Finance Engineer to build data pipelines, dashboards, and automation that power the finance and revenue operations functions. You'll own unit economics, budgeting, financial dashboarding, and deal desk automation while working closely with finance and engineering leadership to ensure the company operates on systems rather than headcount.
What you'll do
- Build and maintain unit economics models for cost-to-serve, gross margin, and contribution by product, customer, and workload
- Own technology budget tracking including cloud, GPU/TPU spend attribution, vendor forecasting, and capacity planning models
- Design and maintain finance dashboards and KPI tracking for leadership and board visibility into burn, runway, and margins
- Automate budget-vs-actuals tracking with live department views and overspend alerts
- Build deal desk framework with pricing guardrails, automated quote generation, and routing logic
- Implement sales compensation automation including comp statements, accelerators, and dispute resolution from CRM and billing data
What they're looking for
- SQL and Python
- Data warehouse modeling and design
- Orchestration tools (dbt, Airflow, or equivalent)
- Financial modeling and unit economics
- Dashboard and visualization tools
- API integration and data pipeline construction
- Cloud cost attribution and optimization
- Internal tools and workflow automation
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AssemblyAI
AssemblyAI builds speech AI models and solutions for developers and enterprises to integrate voice capabilities into their applications. The company is hiring for Applied AI Engineers to implement and support customer deployments, and Developer Relations Evangelists to build community engagement and evangelize its voice AI platform.
- Website
- assemblyai.com
Likely interview questions
- Walk us through a financial model you've built end-to-end—how did you define the metric, validate it, and handle reconciliation issues?
- Describe your experience building data pipelines across multiple source systems (billing, payroll, cloud); how did you handle inconsistencies or missing data?