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San Francisco, California, United States
Onsite
About the Role
Most companies are experimenting with AI in their GTM motion. We are not experimenting. We are building an AI-native revenue organization from scratch, and this is the person who leads that build.
Samba TV's core product is knowing what the world watches, in real time, at scale, across a billion consented devices. The Revenue team should be the most data-intelligent sales org in the industry, with our own data as the unfair advantage. Right now, it is not fully there yet. Your job is to close that gap.
There is a massive amount of signal available across our GTM stack. Gong call transcripts, Salesforce pipeline data, Snowflake viewership tables, Slack conversations, inbound intent signals, People AI data, calendar events, Hubspot leads, renewal timelines. Almost none of it is connected in a way that actually helps a seller do their job better. This role exists to change that. You will build the systems that tie those signals together, eliminate the manual thrashing that slows our team down, and create throughput where there is currently friction.
Claude is our primary AI platform across the Revenue Org. We need someone who is not just a power user but an expert who can architect reliable production systems, train others, and push what is possible in a GTM context. If you have been waiting to work somewhere that treats AI as infrastructure rather than a feature, this is the role.
GTM systems architecture and automation
Design and operate multi-agent systems connecting Salesforce, Gong, Slack, Snowflake, and Samba's proprietary TV data assets into unified, real-time GTM workflows
Build and maintain MCP (Model Context Protocol) servers that give Claude governed, real-time access to our GTM stack, including live Salesforce records, Gong call data, and Samba viewership signals
Eliminate manual data transfer and context-switching for sellers by automating the aggregation, summarization, and routing of deal intelligence across tools
Create RAG pipelines and prompt libraries that give Claude accurate, governed context on our products, prospects, pricing, and competitive landscape
Architect integrations between AI services and internal systems using Python and APIs, enriching contact and account records with live signals
Evals, reliability, and continuous improvement
Design and run structured evals across all production AI systems: measure output quality, accuracy, regression risk, and real business impact, not just vibes
Build eval frameworks that catch prompt drift, model behavior changes, and degraded tool use before they hit sellers in production
Run A/B experiments across workflows to prove what is actually moving pipeline and revenue, not just what looks good in demos
Monitor agentic systems in production and own the feedback loop: what broke, why, and what the fix is
Be the internal authority on Claude across the Revenue Org: best practices, MCP architecture, prompt engineering standards, and enablement
Write documentation and run enablement sessions so sellers and operators extract real value from every system you ship
Signal integration across the sales org
Build systems that automatically surface deal risk, flag renewal exposure, draft personalized outreach, and score accounts by conversion likelihood using real viewership signals
Connect Gong call intelligence, Salesforce pipeline data, and Samba TV audience signals into a unified account view that sellers can act on without digging through five tools
Design workflow automation that reduces the cognitive load on sellers so they spend more time selling and less time updating CRM fields, hunting for context, or writing the same email for the 40th time
Identify the highest-friction points in our GTM process and systematically eliminate them through automation
3 to 5 years in software engineering, data engineering, or GTM/revenue operations engineering with serious technical depth
You have owned an AI or automation function, not just contributed to one
Production-level Python and/or JavaScript; you write, ship, and maintain code others depend on
Hands-on LLM production experience: prompt engineering, tool use, multi-turn agentic workflows, RAG
Experience building and operating MCP servers that connect LLMs to live business systems and data sources
A real eval practice: you know how to measure whether an AI system is working, build regression tests, and catch drift before it causes problems
Deep, production-level Claude expertise including the API, structured outputs, tool use, agentic workflows, and system prompt design; you can also teach it and build reliable systems around it
Hands-on experience with Claude Code as a primary development environment
Salesforce hands-on (SOQL, APIs, Flows) plus at least one sales engagement platform, preferably Gong
Snowflake and dbt working knowledge for querying and transforming data to support AI workflows
Genuine understanding of sales pipeline mechanics: forecasting, deal inspection, pipeline hygiene, ABM
Comfortable in both a command line and a boardroom; you move between implementation and executive conversation without losing either audience
Azure (data services, API management) and Power BI
Slack API and workflow automation (Bolt framework or similar)
AdTech, media measurement, or data-driven intelligence background
LLM orchestration frameworks: LangChain, LlamaIndex, LangGraph
MLOps experience: eval infrastructure at scale, drift monitoring, inference cost management
Prior experience at a high-growth data or media company
