Job Description

Afresh, the AI platform for grocery, began by tackling the most complex problem in the industry: fresh, and has evolved into the core AI platform for grocers. By leveraging proprietary AI designed for high-volatility environments, we empower partners like Albertsons, Meijer, and Wakefern to drive smarter decisions across their entire enterprise. Following record-breaking 70% revenue growth in 2025, we have scaled to 6 enterprise-grade solutions, with solutions live in over 10% of the U. S. grocery market. Our platform now orchestrates billions of decisions from the store floor to the distribution center and prevented over 200 million pounds of food waste last year alone. If you're looking for a role where your work directly translates into massive scale and social good, and you want to be part of the team that defines how the world eats, there is no better time to join us. About the Role Most companies make you choose: build the platform, or go deploy it. Here you do both — and that's the point. 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. Afresh leads the customer relationship and direction; you and a small team bring the technical firepower — scope and architect the work with the customer, then build it.

Because you also own the platform underneath, the rough edges you hit in the field become the things you fix at the root. This is senior, hands-on, 0-to-1 work in a space with no playbook. What You'll Do In the field (forward deployed) - 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. On the platform (building the house you live in) - 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. Across both - Own the flywheel: field learnings flow straight into the platform, and platform improvements show up at the next customer. What Makes You a Great Fit We encourage all highly-qualified candidates to apply, even if they do not fulfill all the listed criteria. - 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).

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