Senior Software Engineer

Proper AI is an AI-first accounting service built for property managers and real estate operators. By combining automation, technology, and a global team of accounting experts, we deliver faster, more accurate financial operations at scale. We’re a team of builders, problem-solvers, and operators from around the world, working together to modernize one of the most critical functions in real estate. Learn more at We are looking for a Senior Software Engineer with strong backend and systems fundamentals to help own the core of our platform: the services, data models, and automations behind workflow and productivity tooling for B2B accounting operations. This is a depth role, not a breadth role. The work is logic-heavy — multi-service data flows, third-party platform integrations, and document-processing pipelines where correctness matters more than surface area. The engineer will be a technical counterpart to the Tech Lead on architecture, and will take ownership of our AI-backed document processing service. The ideal candidate is language-agnostic in outlook but deep in practice: comfortable moving between Go, Python, and TypeScript, and able to build a real working model of an unfamiliar system before changing it. Key Responsibilities Core Functional Responsibilities - Design, build, and maintain backend services across a microservices estate (Go, NestJS/TypeScript, Python). - Own the AI-backed document processing and classification service (Python/FastAPI): extraction quality, accuracy measurement, and the pipeline around it. - Design and maintain integrations with third-party platforms, including authentication, sync cadence, retries, and recovery. - Model data deliberately — schemas, migrations, and invariants that hold as the product changes. - Debug across service boundaries: correlate behaviour across multiple systems and databases to find the actual cause. - Build observability and provenance into automated flows: actions should be traceable, auditable, and reconstructable after the fact. - Design for safe automation: idempotency, pre/postconditions, dry-runs, and guardrails on anything that writes to a customer's system of record. - Write clean, tested, maintainable code, and leave the systems better instrumented than found. Performance and Metrics Tracking - Define and track correctness and reliability measures for owned systems (task success, error and fallback rates, accuracy of automated inference). - Build evaluation coverage for AI-backed output: scenario tests, invariant checks, and regression detection when a model or prompt changes. - Monitor and improve latency, retries, and failure recovery in automated pipelines. Training and Development - Mentor mid-level engineers on system design, debugging methodology, and testing discipline. - Lead design discussions and code reviews. - Document architecture decisions, failure modes, and debugging runbooks. Required Hard Skills - Backend engineering depth — production services in Go and/or Python; TypeScript/Node useful. Multi-language comfort matters more than any single stack. - Data modelling and relational databases — PostgreSQL, schema design, migrations, query performance, and reasoning about data integrity. - Distributed and cross-service debugging — tracing behaviour across services, queues, and databases to isolate a root cause. - Third-party integration engineering — external APIs with imperfect contracts: auth expiry, partial failures, retries, idempotency. - Reliability practice — observability, structured logging, tracing, failure-mode analysis, and recovery design. - Testing and evaluation — unit and scenario tests, invariant checks, and measuring correctness of non-deterministic (AI-backed) output. - Cloud and infrastructure — GCP preferred (Cloud Run, Cloud SQL, Pub/Sub); Docker and CI/CD. - Working with LLM-backed services — using them as components, understan