Staff Software Engineer, Data & AI

Overview Holly is the HR platform built for city and county government. We help local governments modernize how they hire, classify, and manage their workforce — work that directly shapes public services for millions of people. Our platform is live across 12 states with 60+ jurisdictions representing over 10% of the US population, including major counties like Santa Clara and Contra Costa in the Bay Area, Orange County in LA, and Snohomish County in Washington State. Demand is outpacing our team, and we’re building to meet it.

Holly is a seed-stage team of 20+ operators with deep roots in public service, civic tech, and AI. We’ve raised $10M from leading investors in government technology and the future of work and have grown over 100X in the last year. We center public servants and impact in every decision, and we bring that same care to how we work here at Holly.

About The Role We’re hiring a Staff Software Engineer, Data & AI to own the technical direction of Holly’s Data Platform. Local governments publish enormous amounts of public information salary schedules, job classifications, MOUs (Labor Union Agreements), budgets but it’s scattered across thousands of websites and buried in messy formats: scanned PDFs, inconsistent HTML, spreadsheets, and everything in between. Turning that chaos into clean, searchable, trustworthy data is one of our biggest product challenges and deepest moats.

You’ll set direction for the Data Platform ingestion and processing pipelines, canonical datasets, semantic search and retrieval, and inference APIs that product teams build on. AI is part of the infrastructure, not a layer added later. You’ll establish how Holly uses models for extraction, classification, normalization, matching, and search, and how we evaluate and operate those systems in production.

This is a hands-on Staff role with domain-wide scope. You’ll set direction across ingestion, processing, modeling, search, inference, quality, and product consumption; resolve architecture decisions that span teams; and make other engineers more effective on the platform. You’ll work directly with the founders and partner across Product and Engineering to decide which Data Platform investments matter most.

If you’ve worked with large, high-volume data and love the challenge of taming messy real-world inputs into something people can rely on, we’d love to talk.

What You’ll Do You’ll own the technical health of Holly’s Data Platform, lead cross-team decisions, and build the data and AI systems and standards the rest of the product depends on.

Own and Build the Data Platform

• Own the data platform end-to-end from raw public sources to clean, canonical datasets the product consumes
• Design the architecture, schemas, and standards for how Holly ingests, models, and trusts its data
• Partner with the founders to identify, scope, and de-risk our highest-leverage data initiatives
• Set technical direction for the data domain across teams

Build Ingestion & Normalization Pipelines

• Build systems that collect large volumes of public government data from thousands of local-government sources across the web
• Turn messy, heterogeneous inputs scanned PDFs, inconsistent HTML, spreadsheets into structured, normalized data (parsing, extraction, OCR, dedupe, entity resolution, schema mapping)
• Where it adds leverage, incorporate LLM-assisted extraction and embeddings into the pipeline
• Build for freshness, reliability, and scale so data stays current and trustworthy

Model & Serve Data for the Product

• Design canonical data models and domain schemas that product engineers build on
• Expose clean, versioned, well-documented datasets the main app can reliably consume
• Own data quality, validation, lineage, and observability so downstream teams can trust what they’re building on

Own Search & AI Infrastructure

• Set the architecture for semantic search and retrieval over large, changing government datasets
• Design inference APIs for extraction, classification, matching, and other AI-powered data processing
• Establish evals, tracing, fallbacks, and cost controls for model-backed systems
• Create reusable interfaces that let product teams ship Data & AI capabilities safely

Make It Automated & Intelligent

• Evolve pipelines from manual/one-off toward automated, self-healing, monitored systems
• Establish data-quality checks, alerting, and standards that k…