Description
About Lema.AI
Lema.AI is a well-funded, emerging cybersecurity startup based in New York and Tel Aviv, building the first risk engineering platform, turning TPRM analysts from checkbox compliance analysts into risk engineers that save the business. Backed by Team8, Salesforce and F2, and led by proven founders, Lema is redefining how enterprises understand and secure their vendor ecosystem.
Lema is already revenue-generating with dozens of enterprise customers including fortune 500 companies.
More about Lema:
- Lema.ai website
- Business Insider - Cybersecurity startup Lema has come out of stealth to raise $24 million
- Team8 - Why We Invested In Lema
About the TPRM market:
- McKinsey - nearly one-third of recent cyber breaches originated from third parties
- Hitch Partners - Third-party risk dominates security priorities, with AI-enhanced attacks and cloud misconfigurations completing the top three concerns.
About the Role
The role focuses on leading and scaling the support team, including hiring, developing talent, and setting a high performance bar, while staying hands-on with complex escalations. You will own the support process end-to-end and work closely with Product and Engineering to turn customer issues into actionable insights. This includes supporting an AI-powered product across data flows, integrations, and model outputs, building scalable workflows, and driving continuous improvement through key performance metrics.
What You’ll Do
- Lead and grow the team - manage current support engineers and build the function as we scale, including hiring, defining the bar, and developing the team.
- Stay hands-on - take ownership of complex escalations and set the technical standard by example.
- Own the support process end-to-end - from intake and triage to resolution and follow-up.
- Work closely with Product and Engineering - turn customer issues into clear, actionable insights and close feedback loops at the pattern level.
- Support an AI-powered product - investigate issues across data flows, integrations, and model outputs.
- Build for scale - design workflows and runbooks that work today and continue to hold as the team grows.
- Drive with data - define and track key metrics (resolution time, escalation rate, backlog trends) and use them to improve performance.