Technical Support Engineer

Eve is a company focused on redefining legal technology with AI-driven solutions for plaintiff law firms. The Technical Support Engineer role involves investigating AI output quality, diagnosing technical issues, and building support infrastructure while ensuring effective communication with legal professionals.

Responsibilities:

• Investigate AI Output Quality: When customers report issues with AI-generated legal documents, you investigate. You’ll use AI observability tooling to trace model inputs, outputs, and reasoning. You’ll verify claims against source documents and determine whether the issue is a retrieval failure, a data ingestion problem, a prompt issue, or expected model behavior. You clearly communicate your findings to non-technical legal professionals
• Diagnose Technical Issues: Troubleshoot cloud storage sync failures (SharePoint, OneDrive, Dropbox), document formatting and export issues, file handling errors, integration configuration problems, and processing performance issues. Resolve what you can independently and escalate what you can’t with full diagnostic evidence
• Deliver Engineering-Ready Escalations: Every escalation you send to engineering includes an issue summary, trace logs, verified reproduction steps, document context, and business impact assessment. You set the quality bar for how support communicates with engineering
• Operate with Speed: Respond to customer support tickets within SLA. Prioritize ruthlessly. Manage multiple threads without dropping context
• Build the Support Infrastructure: Write SOPs, troubleshooting runbooks, and knowledge base articles. Contribute to our AI agent rollout by optimizing content for AI consumption. Help build the onboarding program for future support engineers. You are joining a team that is actively building its processes, not maintaining them
• Build with AI: Use AI tools daily to accelerate support workflows — drafting responses, analyzing ticket patterns, and diagnosing product behavior. Help shape how Eve deploys AI agents for first-touch triage and self-service resolution. Define what AI-native support looks like in legal tech

Requirements:

• AI Debugging Ability: You can investigate why an AI-generated document produced an unexpected result. You're comfortable navigating AI observability and tracing tooling to understand model behavior. You can distinguish between a retrieval failure, a prompt issue, and a data ingestion problem — and explain the difference to a paralegal
• Technical Depth: You can read logs, trace API calls, debug OAuth token expirations, diagnose cloud storage sync failures, and reason about what's happening under the hood of a SaaS product. You don't need to be a software engineer, but you think like one when troubleshooting
• Structured Escalation Discipline: You document your work with precision. Your bug reports include trace logs, reproduction steps, relevant context, and a clear classification of the issue type. Engineering can pick up your escalation and start working immediately without asking follow-up questions
• Exceptional Communication: You can explain complex technical and AI-specific issues to attorneys who don't care about your stack. You write clearly, concisely, and with empathy. You know when to simplify and when to be precise
• Ownership Mentality: You take personal responsibility for customer outcomes. You follow through until the problem is solved, not just escalated. You don't wait to be told what to do
• 3+ years in a technical support, support engineering, or technical customer-facing role at a SaaS company
• Experience supporting AI-powered or ML-driven products, with exposure to LLM observability or evaluation platforms
• Background in legal technology, law firm IT operations, or professional services software
• Familiarity with APIs, webhooks, OAuth, and integration debugging — especially cloud storage integrations (SharePoint, OneDrive, Dropbox)
• Experience writing scripts (Python, JS) to automate support workflows or analyze data
• Comfort with SQL for querying logs or data analysis
• Understanding of prompt engineering concepts, retrieval-augmented generation (RAG), and LLM behavior patterns
• History of contributing to SOPs, runbooks, knowledge bases, or internal tooling that materially improved team performance
• Ability to work independently in a remote environment while collaborating effectively across team