Job at Modal
Member of Technical Staff - ML Performance
- Employer
- Modal
- Location
- New York · San Francisco
- Workplace
- Not supplied
- Employment
- full-time
- Department
- Engineering
- Salary
- $200,000 to $350,000 per year, as the employer lists it
- Last published
- First listed on USASI
Apply on Modal’s site (external site)View the posting on Modal’s job board (external site)
This posting comes from Modal’s own Ashby job board. USASI checks that board every day and removes this page once the employer closes the posting. The description below is Modal’s text as published; USASI does not edit it, does not recruit, and does not take applications. How jobs are collected.
Job description
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 Ramp (external site), 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 (external site),Luigi (external site)), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
The Role:
We are looking for strong engineers with experience in making ML systems performant at scale. If you are interested in contributing to open-source projects and Modal’s container runtime to push language and diffusion models towards higher throughput and lower latency, we’d love to hear from you!
Requirements:
- 5+ years of experience writing high-quality, high-performance code.
- Experience working with torch, high-level ML frameworks, and inference engines (vLLM or TensorRT).
- Familiarity with Nvidia GPU architecture and CUDA.
- Experience with ML performance engineering (tell us a story about boosting GPU performance — debugging SM occupancy issues, rewriting an algorithm to be compute-bound, eliminating host overhead, etc).
- Nice-to-have: familiarity with low-level operating system foundations (Linux kernel, file systems, containers, etc).
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