Staff Software Engineer

NetDocuments•Lehi, UT

About The Position

NetDocuments is seeking a Staff Software Engineer to help lead the technical direction of one of our backend engineering teams. This is a hands-on technical leadership role for an experienced engineer who thrives on hard problems in scale, performance, reliability, and cost, and who wants their architectural decisions to matter for years. Our platform turns a firm's institutional knowledge into something people and AI can act on. The backend systems that make that possible span large scale data processing, search and retrieval, and the AI powered services that sit on top of the customer's content. You will work at the center of that: designing distributed, cloud-native systems that stay fast, reliable, and secure as data volume grows across regions, all within the strict permissions and data protection legal customers expect. The work is live and growing, and you will partner closely with Product, Design, Platform Services, and engineering leadership to turn complex requirements into durable systems, while helping the team raise its own technical bar. A few principles shape this work and the candidates we are looking for in this role: Build for scale. The interesting problems are in throughput, reliability, and cost as the platform grows. Trusted by design. Everything runs inside the customer's security, permissions, governance, and data protection boundaries. AI forward. We design around what modern models make possible, and we build the data and retrieval foundations that make AI genuinely useful. Measure to improve. We invest in the signals and evaluation that let us make decisions from evidence rather than guesswork.

Requirements

Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
8+ years of professional software engineering experience delivering production-grade systems at scale.
3+ years providing technical leadership and influencing engineering direction and practices.
Deep experience designing and operating distributed, cloud-native backend systems, ideally on AWS (services such as ECS, Lambda, SQS, DynamoDB, and S3).
Strong experience with event-driven and service-oriented architectures, including asynchronous processing, queueing, idempotency, and handling failure and retries.
Proven ability to build systems that are performant, reliable, and cost-effective at high volume.
Strong backend development experience in a modern backend language. C#/.NET is our preferred stack; experience with Python, Java, or Go is also valued.
Experience implementing observability in production: logging, metrics, distributed tracing, and telemetry.
Working familiarity with modern AI and how it is applied in production software.
Experience with automated testing, CI/CD practices, and operating production services.

Nice To Haves

Experience building search, retrieval, or other data-intensive systems at scale.
Familiarity with modern retrieval and AI techniques such as semantic or vector search, embeddings, and retrieval augmented generation.
Experience operating high throughput data or processing pipelines and managing their cost and reliability.
Experience building document management, content services, or other data-intensive enterprise systems.
Experience integrating AI or agent-facing services into production applications.
Experience building B2B SaaS in the legal industry or other regulated, security-sensitive environments.
Contributions to open-source projects, technical writing, or sharing technical expertise with the broader engineering community.

Responsibilities

Serve as a hands-on technical leader for your team, helping define technical direction and engineering standards.
Lead architectural discussions and make technical decisions across services and distributed systems.
Establish patterns for building scalable, secure, reliable, and maintainable systems.
Guide the evolution of the technology stack as products mature and scale.
Drive initiatives that improve system performance, reliability, cost efficiency, developer experience, and engineering quality.
Architect and build production-grade, cloud-native backend services and APIs.
Design event-driven, service-oriented systems and data pipelines that stay correct under load, retries, and partial failure.
Build systems that process, organize, and surface large volumes of content quickly and securely, including search, retrieval, and AI-powered capabilities.
Integrate AI-powered services and emerging technologies that enhance how customers find and act on their information.
Design for security, permissions, and compliance as first-class requirements rather than afterthoughts.
Design resilient systems that stay reliable and performant as volume grows across multiple regions.
Instrument services with logging, metrics, distributed tracing, and telemetry to find and diagnose issues proactively.
Identify bottlenecks and drive improvements in throughput, latency, scalability, and cost.
Design for correctness at scale: idempotency, ordering, deduplication, rate limiting, and safe reprocessing.
Ensure security, performance, and compliance requirements are built into system design.
Own complex technical initiatives from initial design through production deployment and ongoing operation.
Solve challenges across distributed systems, large-scale data processing, search and retrieval, and real-time and batch workloads.
Evaluate architectural tradeoffs across performance, cost, security, scalability, and maintainability.
Create clear technical documentation, including architecture diagrams, system designs, and key technical decisions.
Partner with Product, Design, and other engineering teams on shared infrastructure and integration points.
Mentor engineers across experience levels and help raise technical capabilities and engineering practices across the team.
Build alignment around technical approaches and provide clear direction when decisions need to be made.
Lead knowledge sharing through code reviews, design discussions, documentation, and hands-on guidance.
Stay current with distributed systems, cloud-native architecture, and applied AI.
Research and validate emerging technologies through proof of concepts aligned with product and engineering strategy.
Identify opportunities to improve both customer experience and engineering velocity.
Leverage modern development and AI tooling to increase engineering effectiveness and impact.