Senior Support Engineer

Join Afresh, a company dedicated to reducing fresh food waste in the grocery industry. As a Senior Support Engineer, you will be the go-to person for understanding our platform's behavior and resolving complex technical issues. You will own escalations that others cannot resolve, drive cross-functional change, and mentor junior engineers. This is a senior individual contributor role with a broad scope, allowing you to make a significant impact on the company's success.

• Assumer la responsabilité des escalades complexes et travailler en amont pour éliminer ou minimiser la récurrence future.
• Identifier et diriger des initiatives majeures qui ont un impact critique sur les objectifs de l'équipe.
• Construire des outils et des workflows d'IA pour réduire le volume de tickets et améliorer l'efficacité.

• 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
• 5+ years in technical support engineering, solutions engineering, data engineering, or a comparable role where you personally owned root-cause analysis on production data
• 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
• Self-direction. You source your own answers, set your own milestones, and do not need oversight to make progress
• 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
• Clear, calm written communication, and the instinct to teach rather than correct when helping less experienced teammates
• 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
• Scripting ability in Python or similar to automate investigation and data work
• A measurement instinct. You define success numerically before you start, and you speak to results rather than activity
• Comfort with ambiguity and a bias toward shipping an imperfect, iterative solution over waiting for a complete one
• A track record of mentoring engineers and raising the technical bar of a team
• Experience supporting ML-driven products, and the ability to distinguish whether an anomaly originates in data inputs, model behavior, or application logic
• Experience with Databricks, Zendesk, Stylo, or workflow automation platforms
• Grocery, retail, or supply chain domain knowledge — hands-on familiarity with ordering, replenishment, assortment, or fresh department operations is a significant advantage