Senior​/Backend Engineer, Applied AI

About Hypha

Hypha is an AI-native platform transforming asset management across the full lifecycle—acquisition, management, and exit. Focused on healthcare facilities and multifamily real estate, we’re revolutionizing the industry through three key pillars:

• Observability:
  Providing real-time visibility into asset performance, deal progress, and market conditions for all stakeholders

• Intelligence:
  Enabling AI-driven multidimensional optimization with risk scores, tax strategies, and benchmarking

• Orchestration:
  Creating seamless workflows and a single source of truth to eliminate inefficiencies

As a Senior/Staff Backend Engineer, Applied AI at Hypha, you will own the architecture and evolution of the AI systems at the core of the platform. This includes designing and scaling document processing pipelines, extraction agents, entity resolution systems, computed attribute frameworks, and conversational interfaces that allow users to interact with complex financial data.

You will lead the design of multi-step AI workflows that transform unstructured information into structured, actionable insights, while building the backend infrastructure, evaluation systems, and observability layers required to operate these systems reliably in production. This role is for an engineer who can go beyond implementation and make high-leverage decisions around system design, performance, reliability, and technical tradeoffs.

You will work closely with leadership, product, and engineering peers to shape technical direction, improve engineering standards, and help define the architecture and scaling strategy for Hypha’s applied AI stack over time.

• Architect and scale AI-powered backend systems for document extraction, entity resolution, retrieval, and structured data generation

• Design resilient pipelines, queues, orchestration layers, and failure-handling mechanisms for production AI workflows

• Own data modeling and backend design decisions across PostgreSQL and related storage systems for complex financial and operational data

• Establish observability, evaluation, and debugging standards for AI systems, including tracing, monitoring, quality analysis, and cost/performance tradeoffs

• Improve platform reliability and developer velocity through better abstractions, tooling, and engineering patterns

• Partner with product and frontend teams to design backend capabilities that translate into intuitive product experiences

• Provide technical leadership through architecture reviews, design feedback, and mentorship of other engineers

• 7+ years of backend or full-stack software engineering experience, with meaningful time spent owning backend systems in production

• Strong backend fundamentals — You have deep experience with Node.js/Type Script, PostgreSQL, and building reliable server-side systems in production. You understand concurrency, queuing, data pipeline design, and the tradeoffs involved in scaling backend systems over time.

• Applied AI experience — You’ve built products with LLMs; whether that’s agent architectures, RAG systems, extraction pipelines, or tool-use patterns. You understand the practical challenges of working with non-deterministic systems.

• Systems thinking — You can reason about how components interact  think about failure modes, retries, observability, and cost when designing AI workflows.

• Technical leadership — You help raise the quality bar through architecture decisions, design reviews, code quality, and mentorship of other engineers. You can lead complex backend and applied AI initiatives and improve how the team builds, operates, and scales critical systems over time.

• Pragmatic engineering — You ship working systems, not research prototypes. You know when to optimize and when good enough is good enough.

• AI-fluent — You use AI coding tools daily and know how to wield them effectively as a multiplier on top of strong fundamentals.

• Experience owning architecture for AI-native or data-intensive backend platforms

• Experience designing evaluation, observability, and guardrail systems for LLM-based workflows

• Experience with workflow orchestration tools such as Inngest,…