GTM Knowledge Engineer

Everlaw•Oakland, CA

1d•$133,000 - $169,000•Hybrid

About The Position

As the first GTM Knowledge Engineer, you will be positioned at the intersection of Revenue Enablement, Revenue Operations, and Information Systems within the Go-to-Market Center of Excellence. Your primary responsibility will be to build, deploy, and enhance intelligent interfaces, AI agents, and knowledge systems designed to support commercial teams and streamline the seller workflow. This role involves a dual focus: first, creating and operationalizing GTM agents to automate seller tasks such as meeting preparation, POV generation, certification, stakeholder mapping, and ROI modeling; and second, reducing friction in the quote-to-cash process by translating complex packaging, policy, and process requirements into clear, proactive user-layer guardrails that enable faster and more consistent team performance. Everlaw's mission is to promote justice by illuminating the truth, and our company culture is open and democratic, with a strong commitment to the professional growth of our team members, offering an annual learning and development stipend and regular career check-ins. We encourage applications from individuals who may not meet every listed qualification but believe they can bring value to the team.

Requirements

At least 4 years of relevant professional experience.
At least 1 year of hands-on experience building and shipping AI agents, conversational interfaces, or enterprise-grade internal applications.
Technical proficiency in programming languages (JavaScript, TypeScript, or Python).
Experience interacting directly with APIs, vector storage setups, and AI application orchestration frameworks (Claude, modern LLM APIs).
Proven background engineering practical, lightweight tools that non-technical users adopt smoothly.
Thorough systems-level mapping comprehension of modern B2B SaaS workflows, sales deal milestones (MEDDPICC), data monitoring tracking (SQL, Snowflake data models), and enterprise CRM/CPQ navigation parameters.
Ability to open, read, review, optimize, and debug Javascript or Python code directly to build apps that work seamlessly.
Understanding of information architecture and agent automation as a means to accelerate human capability.
Intense bias for action, excelling at rapid prototyping, thriving under high-growth mandates, and building scalable front-ends that make complex systems instantly actionable for non-technical users.

Responsibilities

Design, code, and ship localized context-aware GTM Agents that solve explicit seller problems: tactical meeting prep, live stakeholder mapping, automatic POV generation, customer champion deck synthesis, and dynamic ROI modeling.
Program and maintain the backend orchestration layers that parse active CRM deal metrics to surface the exact right agent, script, or asset at the millisecond it is needed in a rep's live workflow.
Collaborate with the Content Lead to build interactive, automated AI role-play environments and auto-certification frameworks that adapt automatically to a rep's specific role, target region, or individualized execution gaps.
Prototype real-time sales assistance tools and interactive overlays providing active deal guidance and objection handling support directly inside live call environments.
Work with the rest of the Center of Excellence team, create programmatic integrations and automated pipelines that connect data sources directly into seller interfaces, preserving content freshness and data accuracy automatically.
Master the complete front-end layout of the CPQ platform to map and isolate severe click friction, configuration drop-offs, and administrative delays. Build inline agent validation paths that guide reps step-by-step through complicated calculations (e.g., co-termed add-ons, complex proration structures).
Program proactive data validation scripts and warning triggers that catch item packaging errors or discount threshold violations directly over the user UI, mitigating back-and-forth rework loops with deal desks.
Take abstract, highly complex operational policies and translate them directly into clean, automated rules-of-the-road within user workspaces, using low-overhead interface apps.