Senior Support Engineer I

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.

As a Principal Support Engineer at Afresh, you are the person who understands how our platform actually behaves for our grocery partners. You have deep technical support, solutions, or data engineering experience, you communicate with equal clarity to a store director and a staff engineer, and you are excited to reduce fresh food waste in the grocery industry.

Support Engineering is the complex technical investigation arm of our Support organization —  the last stop for internal escalations to resolve customer-facing problems across all Afresh products. You will own the complex escalations that others cannot resolve and then work upstream to eliminate or minimize future recurrence; quantifying impact, building tooling and agentic AI workflows to deflect future volume, and then proactively driving the cross-functional efforts to eliminate the root cause.

This is a senior individual contributor role, and the scope of this role is deliberately broad. You will be handed goals rather than tasks and you are expected to quickly and autonomously develop deep expertise across our products and the complex data relationships behind them. You will own and lead initiatives from end to end, building influence and alignment with other teams to achieve your goals.

A typical week might involve building an agentic AI support workflow in Stylo, shipping a Claude skill that lets Tier 1 Support resolve issues with a new product feature, coordinating a team-wide investigation of data issues across 200 products, and then presenting the root cause analysis and measured impact of that investigation to Product and Data Engineering leadership. Specifically, you will:

• Own our most complex escalations end to end — data mapping defects, recommendation and forecast anomalies, and integration or data-feed failures
• Identify and lead large initiatives that deliver critical impact against department-level OKRs, such as raising our ticket deflection rate, reducing average handle time, or reducing escalations to cross-functional teams. You will work autonomously — scoping the work yourself, defining what success looks like, and communicating progress and blockers without being asked
• Define and report the numbers that prove impact: baseline before you build, quantify after, and publish on a regular cadence in channels where the appropriate teams and leaders can see it
• Build the tooling that removes the work — SQL and Python, Databricks and Looker dashboards and notebooks, Stylo workflows, Claude and Cowork skills, and lightweight automations that take manual effort off Tier 1 Support and Operations
• Drive cross-functional change with teams that don't report to you, partnering with Solutions Engineering, Data Engineering, Product Engineering, and Operations to fix the systems generating ticket volume — securing shared ownership, holding regular communication cadence, and raising flags visibly when progress stalls
• Multiply the team rather than absorb its hardest work: mentor Tier 1 and Tier 2 engineers, lead training, and write the playbooks and source-of-truth documentation that let others resolve what only you can resolve today
• Be visible outside Support — present in Tech Demos, post data-driven updates in company-wide channels, join account reviews, and represent Support in cross-functional forums
• Master partner-specific behavior across our retailers and their banners, and serve as the escalation point when a judgment call needs to reach Product, Engineering, or the customer directly

The following represent attributes our ideal candidate possesses. We encourage all highly-qualified candidates to apply, even if they do not fulfill all the listed criteria.

• 5+ years in technical support engineering, solutions engineering, data engineering, or a comparable role where you personally owned root-cause analysis on production data
• Advanced SQL. You reason comfortably about joins across large, imperfect datasets, and you validate findings against the authoritative source rather than the most convenient table
• Demonstrated ability to own complex cross-functional initiatives end to end and land them — including the uncomfortable parts: securing commitments from other teams, absorbing friction, and driving to a decision
• A measurement instinct. You define success numerically before you start, and you speak to results rather than activity
• Proficiency building with AI tools such as Claude or other agentic platforms, paired with sound judgment about where automation can be trusted and where it cannot
• Scripting ability in Python or similar to automate investigation and data work
• Clear, calm written communication, and the instinct to teach rather than correct when helping less experienced teammates
• Comfort with ambiguity and a bias toward shipping an imperfect, iterative solution over waiting for a complete one
• Self-direction. You source your own answers, set your own milestones, and do not need oversight to make progress

• Grocery, retail, or supply chain domain knowledge — hands-on familiarity with ordering, replenishment, assortment, or fresh department operations is a significant advantage
• Experience with Databricks, Zendesk, Stylo, or workflow automation platforms
• Experience supporting ML-driven products, and the ability to distinguish whether an anomaly originates in data inputs, model behavior, or application logic
• A track record of mentoring engineers and raising the technical bar of a team

Salary Range in U.S.: $130k to $195k + meaningful early stage equity & benefits