Member of Technical Staff - ML Performance
At a glance
Highlights
- Fast-growing AI infrastructure company
- Series B funding at $1.1B valuation
- Opportunity to contribute to open-source projects
Heads up
- 5+ years minimum
Why this role might suit you
The role suits engineers with deep expertise in GPU‑accelerated machine‑learning performance, offering impact on a high‑scale AI platform, open‑source contributions, and a fast‑moving, well‑funded environment.
Skills
About the role
ABOUT US:
Modal provides the infrastructure foundation for AI teams. With instant GPU access, sub-second container startups, and native storage, Modal makes it simple to train models, run batch jobs, and serve low-latency inference. We have thousands of customers who rely on us for production AI workloads, including Lovable, Scale AI, Substack, and Suno.
We're a fast-growing team based out of NYC, SF, and Stockholm. We've hit 9-figure ARR and recently raised a Series B https://modal.com/blog/announcing-our-series-b at a $1.1B valuation. Our investors include Lux Capital https://www.luxcapital.com/, Redpoint Ventures https://www.redpoint.com/, Amplify Partners https://www.amplifypartners.com/, and Elad Gil https://eladgil.com/.
Working at Modal means joining one of the fastest-growing AI infrastructure organizations at an early stage, with many opportunities to grow within the company. Our team includes creators of popular open-source projects (e.g. Seaborn https://github.com/mwaskom/seaborn, Luigi https://github.com/spotify/luigi), 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).
Compensation
This Machine Learning Engineer role pays $150k-$350k/yr. Within typical range for machine learning engineer roles in United States.
Questions about this role
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