NetDocuments - Staff Software Engineer

• Every document that enters NetDocuments passes through our content extraction and transformation platform, which turns scanned images, PDFs and Office files into searchable text, structured data, and the foundation for AI features like semantic search. We process documents at very high volume, on an event-driven platform built on AWS. • We're looking for a Staff Software Engineer to set the technical direction for this platform and, over time, lead the team that builds it. You'll design the distributed systems that process millions of documents an hour, bring AI-powered extraction into production, and lead an engineering practice where AI agents are part of how we design, build and test software. • This is a hands-on role. You'll write production code, make architecture decisions that hold up over time, and raise the technical bar for the engineers around you. • Technical Leadership & Architecture • Set technical direction for our document content extraction and transformation platform, establishing patterns for scalable, secure, and cost-efficient systems on AWS. • Design and own distributed, event-driven pipelines that process millions of documents an hour, including how they scale, how they fail, and how they recover. • Lead architectural decisions involving OCR technologies such as Tesseract and Apryse, and AI models for document understanding. • Drive improvements in throughput, reliability and performance, while controlling cost per document without affecting customer experience. • Make critical technical decisions that balance performance, security, cost and maintainability, with reasoning that stands up over time. • Build & Operate • Build production services in C#/.NET using AWS (such as ECS, Lambda, S3 and SQS) and event streaming with Kafka. • Design resilient processing: retries, dead-letter handling, idempotency, backpressure, and graceful recovery. No silent failures. • Design and implement scalable APIs (REST, GraphQL) that power our product and enable third-party integrations. • Implement observability through logging, metrics, tracing and telemetry (Datadog, CloudWatch) to find and fix issues before customers notice. • Ensure security, compliance and confidentiality across all implementations. Legal documents are privileged, and our systems treat them that way. • AI in the Product • Build the content pipelines that power semantic search: chunking, embeddings, vector indexing and evaluation, at the scale of millions of documents. • Measure and improve AI output quality with evaluation sets and metrics, while managing cost and latency in production. • AI in How We Build • Lead the team's AI-assisted development practice, using AI agents such as Claude Code across specification, design, testing and implementation. • Set the standards for reviewing and approving AI-generated work so that speed never comes at the cost of quality. • Ownership & Collaboration • Take full ownership of initiatives from concept through production and ongoing evolution. • Proactively identify and address technical debt before it affects the team or customers. • Partner with product management to turn requirements into technical solutions, and with Platform Services, Operations and other teams on shared infrastructure. • Mentor engineers at every level through design reviews, code reviews and documentation. • Build agreement on technical approaches, while having the authority to make the final call when needed. • Research and prove out new technologies through POCs that align with product strategy.