[Hiring] Staff Forward Deployed Engineer @Afresh

Role Description

As a Staff Forward Deployed Engineer, you're part of a single team that both delivers Afresh's AI into enterprise grocery customers and builds the platform that makes that delivery fast. You'll spend dedicated time in the field — embedded with a customer, integrating into their data, shipping AI systems on top of it — and dedicated time on the platform, turning what you just learned into reusable tooling the whole team deploys next. You build the house you live in.

• Partner with Afresh's account lead and the customer's technical teams to scope and architect the work — the data sources, the architecture, and the path to production.
• Embed with the customer's data and engineering teams (remote and on-site); integrate into their cloud and data platform; build production-grade pipelines and model messy enterprise data into trustworthy data products.
• Design and ship LLM- and agent-powered systems on that data — retrieval, agentic workflows, data-quality and analytics agents — reliable enough to run in production, not just to demo.
• Harden what works in the field into the shared platform: the knowledge and grounding layer (knowledge graph, ontology, and retrieval) that makes grocery data usable by LLMs, the agent frameworks, and the serving infrastructure.
• Build the evals, tracing, and tooling that let the team measure quality — accuracy, hallucination rate, latency, cost — and ship faster on the next customer.
• Build for leverage: clean interfaces and reusable building blocks, not one-off per-customer code.
• Own the flywheel: field learnings flow straight into the platform, and platform improvements show up at the next customer.

Qualifications

• 5+ years building production software and data systems, with strong, production-grade code.
• An architect's instinct: you can take an ambiguous problem and a messy data landscape, design a clean and workable solution, and then build it.
• Genuine AI/LLM depth — you've built real systems with LLMs and agents (retrieval/RAG, tool-use) and you evaluate quality rather than eyeball it.
• Real data-engineering depth: building and operating data pipelines, modeling messy enterprise data, and working in a modern cloud data platform (Databricks, BigQuery, Snowflake, or similar).
• Range across both modes — you genuinely like being in front of customers and going heads-down to build reusable infrastructure, and you can switch between them without one suffering.
• Customer-facing comfort: you work well with a customer's engineers and data teams — running working sessions, explaining your thinking, and earning trust through what you deliver.
• A bias toward ownership and momentum, and comfort traveling to customer sites regularly (~10-20%).

Requirements

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

Benefits

• Comprehensive medical, dental, and vision coverage for you and your family, with the majority of premiums covered by Afresh.
• Dedicated mental health support and counseling services.
• Competitive base salary, meaningful equity (U.S. employees), and a 401(k) program with a generous company match.
• Home office stipend and "Coworking Wallets" for flexible workspace access.
• Annual professional development budget to master new skills and grow your career at Afresh.
• Monthly stipends for "Betterment" (wellness/lifestyle) and telecommunications.
• Flexible paid time off to take the time you need to recharge.