Staff Forward Deployed Engineer (AI/LLM)

TL;DR

Staff Forward Deployed Engineer (AI/LLM): Deliver hirify.global’s AI into enterprise grocery customers while building the platform that makes deployments fast, with an accent on production-grade LLM/agent systems, data pipelines, and evaluation/quality measurement. Focus on turning messy customer data into trustworthy data products and hardening field learnings into reusable knowledge/grounding, agent frameworks, and serving infrastructure.

Location: Hybrid — San Francisco office (2 days/week). Regular travel to customer sites (~10–20%).

Salary: $168,912–$273,368 (USD)

Company

hirify.global builds an AI platform for grocery to reduce food waste and help grocers make smarter decisions.

What you will do

• Scope and architect customer deployments with account leads and customer engineering/data teams, defining data sources, architecture, and path to production.
• Embed with customer teams to integrate into their cloud/data platforms and build production-grade pipelines that turn messy enterprise data into reliable data products.
• Design and ship LLM- and agent-powered systems (retrieval/RAG, agentic workflows, data-quality and analytics agents) that run reliably in production.
• Harden field learnings into the shared platform: knowledge/grounding layer (knowledge graph/ontology + retrieval), agent frameworks, and serving infrastructure.
• Build evals, tracing, and tooling to measure quality (accuracy, hallucination rate, latency, cost) and accelerate iteration across customers.
• Own the flywheel so field learnings feed the platform and platform improvements show up in the next customer deployment.

Requirements

• 5+ years building production software and data systems with strong production-grade code.
• Ability to take ambiguous problems and messy data landscapes, design a clean solution, and build it.
• AI/LLM depth: built real LLM/agent systems (retrieval/RAG, tool-use) and evaluated quality (not just demos).
• Data-engineering depth: build and operate data pipelines and work in modern cloud data platforms (e.g., Databricks, BigQuery, Snowflake).
• Comfort switching between forward-deployed customer work and building reusable platform infrastructure.
• Customer-facing comfort working with engineers/data teams and earning trust through delivered outcomes; travel to customer sites regularly (~10–20%).

Nice to have

• Experience in grocery, retail, or supply chain data domains.
• Production knowledge graphs/ontologies/semantic layers; graph and vector stores (pgvector, Pinecone, Weaviate) and hybrid search.
• MCP or similar tool/context protocols; agent frameworks (e.g., LangGraph); MLOps/model serving/observability for LLM systems.
• Prior forward-deployed solutions/implementation engineering or early-stage startup experience.

Culture & Benefits

• Comprehensive medical, dental, and vision coverage for you and your family, with most premiums covered.
• 401(k) with generous company match and meaningful equity for U.S. employees.
• Home office stipend and flexible workspace access (“Coworking Wallets”); monthly wellness/lifestyle and telecommunications stipends.
• Flexible paid time off and dedicated mental health support.
• Hybrid work with support for working from home or local office; full-time U.S. employees eligible for benefits.

Hiring process

• Interviews focused on production software/data systems, LLM/agent experience, and ability to deliver both customer deployments and reusable platform improvements.
• Evaluation of technical depth via discussions of real systems, quality/evals, and architecture decisions.