Skip to main content

Modal

Talent Partner, GTM

San Francisco, California, United States +1

Apply on Modal’s site ↗View original ad
Location
San Francisco, California, United States +1
Work
On-site
Employment
Full-time
Level
Not stated
Function
People & HR
Pay
$180K – $220K a year
Posted
4 weeks ago

About the role

Modal builds the infrastructure that lets engineers run AI workloads without the usual pain. To do this well, we need exceptional people, and that’s where you come in.

Many pages were opened from here in the last minute, so the full ad isn’t loaded. It’s on the company’s site. Read it there ↗

Source

Posted by Modal on their own hiring page, jobs.ashbyhq.com. OptiTalent shows it as published; the original is the reference.

View the original ad ↗

More at Modal

  • Talent Partner, Technical

    8 days ago

    Modal · New York, United States

    Full-time$180K – $220K a yearPeople & HR

    Modal builds the infrastructure that lets engineers run AI workloads without the usual pain. To do this well, we need exceptional people – and that’s where you come in.

  • Head of IT

    3 weeks ago

    Modal · New York, United States

    On-siteFull-timeDirector$270K – $320K a yearIT

    We’re looking for a Head of IT to build and own the systems that enable everyone at Modal to work effectively and securely.

  • GTM Engineer - Data Platform

    3 weeks ago

    Modal · New York, United States +1

    On-siteFull-time$200K – $220K a yearData & AI

    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…

  • Member of Technical Staff - Inference Runtime

    4 weeks ago

    Modal · San Francisco, California, United States

    On-siteFull-timeLead / staff$220K – $300K a yearEngineering

    Modal’s Inference Runtime team owns the container runtime stack used to run inference and training workloads across our fleet. We work at the boundary of Linux, containers, filesystems, storage, GPU drivers, and distributed systems.