Senior Forward Deployed Engineer

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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 Senior 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. 3+ 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 witho

Afresh，这家面向杂货行业的AI平台，最初从解决行业内最复杂的问题——生鲜——入手，如今已发展成为杂货商的核心AI平台。通过利用专为高波动环境设计的专有AI，我们赋能Albertsons、Meijer和Wakefern等合作伙伴，在其整个企业范围内推动更明智的决策。继2025年实现创纪录的70%收入增长后，我们已扩展至6个企业级解决方案，覆盖美国杂货市场超过10%的份额。我们的平台如今协调从门店到配送中心的数十亿次决策，仅去年一年就防止了超过2亿磅的食物浪费。如果你正在寻找一个工作成果能直接转化为大规模影响和社会公益的职位，并且希望成为定义世界饮食方式的团队的一员，那么现在正是加入我们的最佳时机。关于该职位 大多数公司让你二选一：构建平台，还是去部署平台。在这里，你两者都做——而这正是关键所在。作为高级前置部署工程师，你属于同一个团队，既负责将Afresh的AI交付给企业杂货客户，也负责构建让交付更快的平台。你将投入专门的时间在客户现场——嵌入客户团队，整合他们的数据，在其上交付AI系统——同时投入专门的时间在平台上，将你刚学到的东西转化为整个团队下一次部署可复用的工具。你建造自己居住的房子。Afresh主导客户关系和方向；你和一个小团队提供技术火力——与客户一起界定范围并设计架构，然后将其构建出来。由于你同时拥有底层平台，你在现场遇到的粗糙边缘将成为你从根本上修复的对象。这是一个高级、动手实践的0到1工作，在一个没有现成剧本的领域。你将做什么 在现场（前置部署）与Afresh的客户负责人和客户的技术团队合作，界定范围并设计架构——数据源、架构以及通往生产的路径。嵌入客户的数据和工程团队（远程和现场）；整合到他们的云和数据平台；构建生产级管道，将混乱的企业数据建模为可信赖的数据产品。在这些数据上设计和交付由LLM和智能体驱动的系统——检索、智能体工作流、数据质量和分析智能体——足够可靠以在生产中运行，而不仅仅是演示。在平台上（建造你居住的房子）将在现场行之有效的东西硬化到共享平台中：使杂货数据可供LLM使用的知识和 grounding 层（知识图谱、本体和检索）、智能体框架以及服务基础设施。构建评估、追踪和工具，让团队能够衡量质量——准确性、幻觉率、延迟、成本——并在下一个客户上更快交付。为杠杆而构建：干净的接口和可复用的构建块，而不是每个客户的一次性代码。跨越两者 拥有飞轮：现场学习直接流入平台，平台改进在下一个客户身上体现。为什么你非常适合 我们鼓励所有高素质候选人申请，即使他们不完全满足所有列出的标准。3年以上构建生产软件和数据系统的经验，具有强大的生产级代码 架构师的直觉：你能够接受一个模糊的问题和混乱的数据景观，设计一个干净可行的解决方案，然后将其构建出来。真正的AI/LLM深度——你用LLM和智能体（检索/RAG、工具使用）构建过真实系统，并且你评估质量而不是凭肉眼判断。真正的数据工程深度：构建和运营数据管道，建模混乱的企业数据，并在现代云数据平台（Databricks、BigQuery、Snowflake或类似平台）中工作。跨越两种模式的能力——你真正喜欢面对客户并埋头构建可复用基础设施，并且你能够在两者之间无缝切换

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