AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We've passed $1B in annualized run rate, up 15x from a year ago. In October 2026 we raised a $750M Series D led by Accel at a $15.75B valuation.
Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
GTM Engineering is responsible for the data, business logic, workflows, and agents that enable our Marketing and Sales teams to help customers get the most out of Modal.
Like the infrastructure beneath the Magic Kingdom, much of our work happens behind the scenes. We build systems that anticipate and deliver what customers need to grow on Modal.
We bring a product management, growth engineering, and data science approach to GTM, from centralizing our data platform to building agents for account research.
The Role:We’re looking for a GTM Engineer to own our GTM data platform. This role combines analytics engineering with revenue operations: you’ll build the org map, people graph, and data systems that help us discover future customers and support our GTM workflows, partnering closely with our Data team on the shared foundation.
What You’ll Work On:Build and maintain an org map and people graph connecting companies, labs, teams, and individuals to identify researchers, engineers, and early-stage startups, including people beyond our existing users and CRM
Own identity resolution, enrichment, and CRM schemas so affiliations, roles, and relationships stay accurate, current, and traceable to their sources
Build GTM data models in Snowflake and dbt connecting people, companies, workspaces, workloads, usage, and revenue, giving reps and agents a shared foundation for customer context
Partner with RevOps and Marketing to define and implement metrics, data models, and business rules for routing, territory assignments, compensation, pipeline reporting, and attribution
Partner with Data on usage forecasts and account signals; build reproducible reporting that reconciles actuals, forecasts, human adjustments, and credited attainment
Build reverse ETL pipelines for ad audiences, lifecycle campaigns, CRM enrichment, outbound, and event outreach, preserving engagement history and campaign eligibility
Own data quality and system health, from graph coverage and stale enrichment to failed syncs and historical reporting accuracy
Build the tooling that helps GTM Engineering ship reliably, from development environments, automated tests, and deployments to backups and shared AI agents and skills
You’re fluent in SQL and proficient in Python
You already use AI coding tools and agents to build, debug, and automate your work, and help teammates do the same
You’ve built data models and production pipelines with tools like Snowflake, dbt, Modal, or Hex
You have hands-on experience with CRM architecture, integrations, and revenue or marketing operations
You understand how leads, accounts, opportunities, and revenue move through a business
You have high attention to detail and a habit of testing, documenting, and monitoring the systems you build
Experience with usage-based businesses
Skills Required
- SQL fluency
- Python proficiency
- Experience using AI coding tools and agents
- Experience building data models and production pipelines with tools such as Snowflake, dbt, Modal, or Hex
- Hands-on experience with CRM architecture, integrations, and revenue or marketing operations
- Understanding of leads, accounts, opportunities, and revenue flows through a business
- Experience with usage-based businesses
- High attention to detail and a habit of testing, documenting, and monitoring systems
What We Do
Deploy generative AI models, large-scale batch jobs, job queues, and more on Modal's platform. We help data science and machine learning teams accelerate development, reduce costs, and effortlessly scale workloads across thousands of CPUs and GPUs. Our pay-per-use model ensures you're billed only for actual compute time, down to the CPU cycle. No more wasted resources or idle costs—just efficient, scalable computing power when you need it.
