# Founding Senior Manager, Forward Deployed Engineering at Afresh > Remote · United States · Manager · Software engineering · open. Source: https://alderhq.app/jobs/greenhouse%3Aafresh%3A6200751004 (read 2026-11-06). ## Facts (observed) - Company: [Afresh](https://alderhq.app/companies/afresh) - Location text: San Francisco, CA; Remote in the U.S.; San Francisco, CA - Where it can be done from (inferred): Remote · United States (evidence: "Location: San Francisco, CA; Remote in the U.S.; San Francisco, CA") - Posted: 2026-09-18 (48 days ago) - First seen by alder: 2026-11-03 · last verified: 2026-11-03 - Reposted: 0 times · edited: 0 times - Apply on the employer's site: https://job-boards.greenhouse.io/afresh/jobs/6200751004 ## Pay and sponsorship - Posted pay: not posted - Market band for similar posted roles (SF Bay Area, n 73): p25 $188k, median $225k, p75 $262k - H-1B filings at this company for similar roles, FY26: 1 ([US DOL OFLC LCA disclosure data](https://www.dol.gov/agencies/eta/foreign-labor/performance)) ## Description (the employer's text) 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. Location: This is a hybrid role requiring 3 days / week in San Francisco + travel to customers ### About the Role Afresh is building the team that deploys our AI inside the largest grocers in the US. You'll build and lead the team of forward deployed engineers who embed with our enterprise grocery customers, integrating into their data, shipping AI systems on top of it, and hardening what works into our platform. You'll own the technical relationship with each customer's engineering leaders, and you'll partner with the Deployment Strategist who owns the account to plan and sequence the work. This is a player-coach role with a strong bias toward leadership. You'll review architecture, unblock your engineers, and get into the code when an account needs it, but most of your week goes to leading the team's execution and to customer stakeholders rather than to personally building. This team will be part of an existing AI engineering group, but will be built and scaled from the ground up. We're looking for an architect rather than a specialist. You should be able to hold a grocer's data estate and our platform in view at the same time and judge where the two meet, without needing to be the deepest expert in any single layer. ### What You'll Do ### Lead the team - Hire, develop and retain forward deployed engineers. Set the technical bar and the standard for how the team works with customers. - Lead execution across concurrent customer engagements: what gets built, in what order, and to what quality bar. - Staff engagements across accounts, making resourcing calls that balance customer need, team development and what the platform needs next. - Run regular 1:1s and invest in career growth. Several of our engineers are strong builders doing customer-facing work for the first time. - Stay hands-on enough to review architectures and code, lead technical discovery sessions, and debug alongside your team when an account calls for it. ### Own the technical relationship - Be the senior technical partner to each customer's engineering and data leadership. Set the delivery roadmap with them, defend the architecture behind it, and tell them early when something slips. - Partner with the Deployment Strategist on overall account execution. They own the customer relationship and the commercial picture; you own the technical plan and whether it lands. - Turn a broad business goal, like reducing shrink, into a scoped program with a sequence, a cost and a date. - Work out how each customer actually operates, most of which is undocumented: what they mean by an "item," who signs off on a markdown, which exception path the DC team uses every week. The real constraint is usually not the stated objection. ### Own the architecture across accounts - Own one reference architecture for AIE that holds across deployments, covering our current hosted default and the customer-cloud exceptions, so platform features ship once rather than once per customer. - Decide what gets hardened into the shared platform, what gets built for a single customer, and what we decline to build. - Set production standards for reliability, security review, data residency, and the evals and tracing that show whether a deployed AI system is actually working. --- Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/greenhouse%3Aafresh%3A6200751004 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.