Senior Manager, Technical Support Engineering | Eve · Teeming.ai
Eve
Eve optimizes your casework from intake to resolution.
Seamlessly integrate an AI case assistant that optimizes your firm into an AI-native powerhouse - securely and responsibly.
Eve optimizes your casework from intake to resolution.
Seamlessly integrate an AI case assistant that optimizes your firm into an AI-native powerhouse - securely and responsibly.
Product: AI platform for plaintiff law firms covering intake, drafting, litigation support, and automated auditing
Customers: Claims 1,000+ plaintiff-firm customers
Compliance: SOC 2 Type II and HIPAA compliance claimed; emphasizes firm-level data isolation
Founders: Jay Madheswaran, Matt Noe, David Zeng
Recent financing: Reported Series B led by Spark Capital (Dealroom reports $103M, $1B+ valuation)
Company Overview
Problem Domain
Legal technology for plaintiff law firms; casework automation and AI-assisted legal drafting and review.
Industry
Technology, Information and Internet
Funding Track Record
Series A
$47M
Company announced a $47M Series A
Series B
$103M
Reported Series B that Dealroom states valued the company above $1 billion
Investor Signal
“Spark Capital led Series B”
Founders
What we do
Join the Team
Senior Manager, Technical Support Engineering
RemoteUS
Remote • US
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About Eve
Eve is redefining legal technology for plaintiff law firms, and we're building the team that will take us there. We help firms handle more cases, recover more for clients, and grow with AI that works across every stage of a case, from intake through resolution. The next generation of great plaintiff firms will be AI-Native, and Eve is how they get there. But what makes Eve different isn't just the product. It's how we build it. If you're someone who takes ownership, stays curious, and wants to build AI that's already changing how law is practiced, this is where you belong.
Product-market fit:
Eve is trusted by over 1000+ law firms, and we’re growing fast.
Backed by top investors:
We’ve raised over $160M from world-class partners including Spark Capital, Andreessen Horowitz(A16z), Menlo Ventures, and Lightspeed.
Built by a world-class team:
Engineers, designers, and operators from places like Scale, Meta, Airbnb, Cruise, Square, Rubrik, and Lyft are building Eve from the ground up.
AI-Native from day one:
We’re on the bleeding edge of AI, collaborating directly with teams at OpenAI and Anthropic to build best-in-class AI workflows tailored for legal work.
Explosive growth:
We are growing 2X revenue Quarter over Quarter.
What You'll Do
What We're Looking For
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.
Preferred Qualifications
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
Benefits
💰 Competitive Salary & Equity
💹 401(k) Program with Employer Matching
⚕️ Health, Dental, Vision and Life Insurance
🩼 Short Term and Long Term Disability
🚗 Commuter Benefits*
🧑💻 Autonomous Work Environment
🖥️ Workplace Setup Reimbursement
🏠 Telecomm Stipend
🏝 Flexible Time Off (FTO) + Holidays
🚀 Quarterly Team Gatherings
🥪 In office Perks*
In office employees only
Eve Legal is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, veteran status, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation during the application process, reach out to your recruiter.We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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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.
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.
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
