Everlaw
GTM AI Engineer
About this role
Build and scale AI-powered decision intelligence systems for Go-to-Market operations at Everlaw. You'll design conversational AI agents, workflow automations, and analytics capabilities that transform enterprise data into actionable insights for Sales, Marketing, and Revenue Operations teams.
What you'll do
- Design and deploy conversational AI experiences, intelligent assistants, and context-aware agents for GTM workflows
- Build and operate AI agents that automate approvals, data maintenance, reporting, and process investigations
- Develop decision intelligence capabilities to proactively monitor operations and flag risks and opportunities
- Own the GTM AI strategy, roadmap, and unified knowledge architecture with governed semantic layers
- Establish AI governance practices including security, privacy, and responsible AI guardrails with performance monitoring
- Lead cross-functional adoption initiatives and manage AI tool vendor rationalization
What they're looking for
- LLM application development (OpenAI, Anthropic Claude, or equivalent)
- AI agent design with tool/function calling and multi-step workflows
- Production AI system evaluation and monitoring for accuracy, hallucinations, and cost
- Python, SQL, JavaScript, or TypeScript
- GTM analytics, revenue operations, or business systems experience
- Systems thinking and B2B SaaS commercial process expertise
- Prompt design and context management
Benefits
- Comprehensive medical, dental, and vision coverage
- Flexible Spending Accounts for health and dependent care
- Mental health support through Modern Health membership
- Paid parental leave
- Company-sponsored life and disability insurance
- Competitive equity grants
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Everlaw
Everlaw builds a platform for litigation that handles sensitive case data and enables sales teams through AI-powered automation. The company is hiring for security engineers to protect its infrastructure and GTM knowledge engineers to develop AI agents that streamline sales workflows and improve quote-to-cash processes.
- Website
- everlaw.com
Likely interview questions
- Walk us through a production LLM application you've built end-to-end—what was the business problem, which model did you choose, and how did you handle hallucinations or accuracy issues?
- Describe your experience designing and orchestrating multi-step AI agents with tool calling. What were the biggest challenges in keeping costs manageable and latency acceptable?