Senior Manager, Technical Support Engineering

Eve is redefining legal technology for plaintiff law firms, and they're looking for a Senior Manager of Technical Support Engineering to lead their support engineering team. This role involves building and scaling the team, setting quality standards for escalations, investigating AI output quality, and optimizing operations for AI-native support.

Responsibilities:

• Build and Scale the Support Engineering Team: Hire, develop, and manage a team of Technical Support Engineers. Build the onboarding program that gets new engineers productive. Create career paths for technical ICs who want to grow without leaving support. Establish a team culture rooted in technical rigor, AI fluency, and customer empathy
• Set the Escalation Quality Standard: Define what a complete, engineering-ready escalation looks like: issue summary, trace logs, verified reproduction steps, document context, and business impact assessment. Close the gap between your best escalations and your average. Make escalation quality a measurable, coached discipline. This is the single most important operational problem to solve in your first 90 days
• Investigate AI Output Quality: A large portion of your team’s work involves investigating why the AI produced a specific output. You’ll build the frameworks and tooling workflows that enable your team to use AI observability platforms to trace model inputs, outputs, and reasoning. You’ll train your team to distinguish between retrieval failures, data ingestion problems, prompt issues, and expected model behavior, then communicate those findings clearly to attorneys who don’t care about your stack
• Build AI-Native Support Operations: Lead the rollout of AI agents for first-touch triage and self-service resolution. Optimize the knowledge base for AI consumption. Define what AI-native support looks like in legal tech, where AI handles routine diagnostic work and your team focuses on complex investigations
• Own the Engineering Relationship: Make engineering trust your team’s triage quality. Your escalations should be so complete that engineers can start working immediately without asking follow-up questions. Ensure all customer-reported issues route through support first, and that support is the fastest, most reliable path to resolution. Build direct relationships with engineering leads
• Build the Measurement System: Establish SLAs, first response time, resolution time, escalation quality scoring, ticket deflection rate, customer satisfaction, incident management, and on-call rotation. Present ticket trends, failure mode patterns, and capacity data to leadership. Your reporting tells leadership what’s actually happening in the product

Requirements:

• Technical Support Leadership: You've built or significantly scaled a technical support team at a SaaS company. You know how to hire, onboard, coach, and develop support engineers. You've managed team performance through metrics
• AI Product Fluency: You understand how AI-powered products work well enough to lead a team investigating AI output quality. You can distinguish between a retrieval failure, a prompt issue, and a data ingestion problem. You don't need to be an ML engineer, but you need to credibly lead people who debug AI behavior daily
• Escalation Quality Obsession: You've personally set the standard for how support communicates with engineering. You know what a complete bug report looks like, and you've built systems to ensure your team consistently delivers that standard
• Builder Who Still Debugs: You've built support infrastructure before: SOPs, runbooks, knowledge bases, onboarding programs, quality frameworks, AI agent workflows. You also stay close to the technical work. You jump into issues, explore logs, and conduct hands-on testing when needed
• Operational Rigor: You think in systems. SLAs, capacity models, ticket categorization, deflection strategy, CSAT measurement. You build the operating model and hold the team accountable
• Cross-Functional Credibility: Engineering needs to trust that your team's escalations are complete and accurate. Product needs to trust that your ticket data reflects real patterns. You build that trust by consistently delivering quality
• Customer Empathy: Your customers are plaintiff attorneys, paralegals, and legal operations professionals. They are not developers. They need clear, jargon-free communication and fast resolution. You build a team that communicates at their level
• 5+ years leading or managing technical support teams at a SaaS company, with experience scaling a team from early stage to operational maturity
• Experience supporting AI-powered or ML-driven products, with exposure to LLM observability, evaluation platforms, or AI quality assurance workflows
• Track record of building support infrastructure from scratch: SOPs, escalation frameworks, quality scoring, knowledge bases, onboarding programs
• Experience deploying AI agents or automation to improve support efficiency and ticket deflection
• Familiarity with support tooling ecosystems (ticketing platforms, knowledge management, AI observability tools, bug tracking systems)
• Comfort with data analysis: SQL for querying logs, building dashboards, and identifying trends in ticket data
• Experience managing the support-engineering relationship, including establishing escalation protocols and quality standards
• Understanding of cloud storage integrations (SharePoint, OneDrive, Dropbox) and common failure modes in document-heavy SaaS products
• History of developing individual contributors into senior technical roles with clear career progression frameworks
• Experience building and maintaining team standards in a fully remote environment