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Applied Engineer

DualEntry - New York, NY, USA - In-office - posted 2026-08-11

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Salary: USD 160,000 - 270,000 / annual

DualEntry is building an AI-native ERP platform that automates accounting workflows from data entry to close. The company has raised $100M+ from top-tier VCs (Lightspeed, Khosla Ventures, Contrary, Google Ventures) and serves customers ranging from $5M ARR startups to publicly traded enterprises. As an Applied Engineer, you will embed directly on named customer projects and own them end-to-end: discovery, design, build, deploy, and verification. Your responsibilities include building migration and integration pipelines using Temporal that are idempotent, resumable, and auditable; modeling and loading customer accounting data into Postgres correctly while reconciling against existing systems; creating internal tooling to eliminate recurring manual work; and feeding patterns back into the core product for productization. You will need 5+ years of production full-stack software experience shipping unsupervised. Required skills include Django and DRF with deep Postgres knowledge (query plans, live migrations), Next.js and React with TypeScript, and durable workflow experience (Temporal preferred, Celery acceptable). Direct customer exposure is essential—you should have scoped problems with customers and shipped solutions. Experience with third-party APIs, webhooks, rate limiting, and reconciling disagreeing systems is required. Nice-to-have qualifications include accounting, ERP, or fintech domain knowledge (double-entry bookkeeping, chart of accounts, close processes, AP/AR, revenue recognition), prior forward-deployed or applied engineering experience, and judgment about when to ship a script versus build a system. You'll join a high-trust, high-speed environment with significant equity ownership in a top AI company. Your feedback shapes the product directly, and you'll grow with the company from an early stage. The team includes accomplished engineers who have shipped major features and systems in compressed timeframes.

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