GTM Role (General - Multiple Positions)

Overview

You'll be working on Domino's GTM team selling their Enterprise AI Platform to data science and ML engineering leaders at Fortune 500 companies. This is enterprise software that helps companies build, deploy, and monitor AI models at scale. You're talking to VPs of Data Science, Chief Data Officers, and ML platform teams about consolidating their fragmented AI tooling.

Role Snapshot

Company Context

Stage: Later-stage private (248 employees suggests Series C/D)

Size: 248 employees

Growth: Actively hiring across GTM after consolidating RevOps and CS under one leader

Market Position: Established player in enterprise MLOps - competing in a crowded but growing category with companies building serious AI infrastructure

GTM Reality

Pipeline Sources:

• 30% Inbound - mostly from existing relationships, analyst reports, word-of-mouth in data science community
• 60% Outbound - targeted account-based selling into F500 data science teams
• 10% Partners - some integration partners and consulting firms

SDR/AE Structure: Likely has SDR support for account research and initial outreach, but AEs are deeply involved early given technical complexity

SE Support: Definitely have Solutions Engineers/Architects - you can't sell MLOps without deeply technical demos and POCs

Competitive Landscape

Main Competitors: Likely competing against Databricks MLflow, SageMaker, internal build solutions, and other MLOps platforms

How They Differentiate: End-to-end platform play vs point solutions, focus on governance and collaboration for large teams

Common Objections: "We're already using open source tools", "We have data scientists who can build this", "We're committed to AWS/Azure/GCP native tools"

Win Themes: When teams have grown past homegrown solutions and need governance, when multiple teams need to collaborate, when models need to go to production reliably

What You'll Actually Do

Time Breakdown

Prospecting (20%) | Active Deals (40%) | Technical/Internal (40%)

Key Activities

• Account Research: Identifying companies with 10+ data scientists who are likely outgrowing their current setup. You're looking for signals like multiple AI initiatives, recent hiring of ML roles, or mentions of model deployment challenges.
• Multi-Threading: Managing 6-10 active deals with 4-8 stakeholders each. You're coordinating between the VP of Data Science (economic buyer), ML platform engineers (technical buyer), and IT/security (blocker) across months of conversations.
• POC Babysitting: Shepherding proof-of-concepts where their data science team tests Domino for 30-60 days. You're checking in weekly, troubleshooting issues with your SE, and trying to generate a clear success metric.
• Procurement Navigation: Once they want to buy, you're waiting 6-8 weeks for legal, security reviews, vendor forms, and budget approvals. Most of your deals will slip at least one quarter.

The Honest Reality

What's Hard

• Data scientists are skeptical of vendor tools - many prefer open source and think they can build this themselves. You're selling to technical buyers who will scrutinize your architecture.
• Deals involve security reviews, compliance checks, and IT infrastructure discussions that add months to the cycle. You'll lose deals to "no decision" when priorities shift.
• The consolidation of RevOps and CS suggests they're tightening up processes, which likely means more pipeline scrutiny and forecast accuracy pressure.

What Success Looks Like

• Landing 2-3 new enterprise logos per year at $300K+ ACV
• Expanding existing accounts as more data science teams adopt the platform
• Getting through POCs where they actually deploy models using Domino (not just kick the tires)

Who You're Selling To

Primary Buyers:

• VP/Director of Data Science or ML Engineering (owns the pain of scaling their team)
• Chief Data Officer or Chief AI Officer (has the budget and strategic mandate)
• ML Platform Engineers (technical validation, will stress-test your product)

What They Care About:

• Can their team actually use this without abandoning their preferred tools (Python, R, specific libraries)?
• Does it handle their security/governance requirements (model versioning, audit trails, access controls)?
• Will it integrate with their existing stack (cloud provider, data warehouses, CI/CD tools)?

Requirements

• Experience selling technical B2B software to technical buyers (data/engineering personas)
• Ability to learn enough about ML/data science workflows to have credible conversations (you don't need to code, but you need to understand the problems)
• Patience for long enterprise sales cycles with multiple stakeholders and technical evaluations
• Comfort with POC-driven sales where the product has to prove itself in their environment
• Track record of navigating procurement, security, and compliance processes at large companies