People Operations Generalist

Posted Yesterday
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New York, NY, USA
In-Office
115K-145K Annually
Junior
Machine Learning • Generative AI
The Role
Manage end-to-end employee lifecycle from onboarding through offboarding, serve as first contact for HR questions, maintain self-service resources, support People program rollouts, run employee experience events, and process employee changes accurately while building scalable People operations processes and documentation.
Summary Generated by Built In
About Us:

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 Lovable, 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 recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.

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.

The Role:

We're looking for a People Operations Generalist to join our growing People team. You'll touch the employee lifecycle end-to-end — from offer acceptance through offboarding — while helping to build the processes and documentation that let our People function scale with the business.

This is a great fit for a highly organized, systems-oriented people person who thrives in a fast-paced environment and wants to build operational foundations, not just maintain them.

What you’ll do

  • Own and continuously improve the new hire onboarding experience, ensuring employees are set up for success and internal tasks are tracked and completed on time.

  • Serve as a first point of contact for employee questions across the full HR spectrum, triaging and routing more complex issues to the right People team member or external partner.

  • Maintain and improve self-service resources (FAQs, Notion pages, etc.) so employees can find what they need quickly — and always feel supported even when no one's in the room.

  • Support rollouts of new People programs and policies, including communication planning, manager enablement, and documentation.

  • Help bring employee experience programs and events to life, from team offsites to milestone recognition, that reinforce culture and make people feel valued.

  • Process employee changes (promotions, equity refreshes) accurately and on time, including preparing updated employment letters and maintaining compliant employee files.

Requirements:
  • 2+ years of People Operations experience, preferably at a high-growth startup.

  • Demonstrated ability to build and document HR processes from the ground up, not just execute existing ones.

  • Strong written communication: you write clearly, keep things simple, and know your audience.

  • Experience managing employee data with accuracy and discretion.

  • Comfortable handling competing priorities without losing track of the details.

Skills Required

  • 2+ years of People Operations experience
  • Experience at a high-growth startup
  • Ability to build and document HR processes from the ground up
  • Strong written communication
  • Experience managing employee data with accuracy and discretion
  • Ability to handle competing priorities without losing track of details
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The Company
HQ: New York City, New York
50 Employees

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.

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