Member of Technical Staff - ML Performance

New York City, New York, United States$200k–350kFull-timePosted 6 months ago

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Member of Technical Staff - ML Performance

New York City, New York, United States$200k–350kFull-timePosted 6 months ago

Modal

Apply

Experience asked

5+ yrs

Degree asked

Not stated

Languages required

Not stated

Work permit

Not stated

Posted

6 months ago

21 Apr 2026

Asks for

The ad says“Familiarity with Nvidia GPU architecture and CUDA.”
The ad says“5+ years of experience writing high-quality, high-performance code.”
The ad says“Experience working with torch, high-level ML frameworks, and inference engines (vLLM or TensorRT).”
The ad says“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).”
The ad says“Familiarity with Nvidia GPU architecture and CUDA.”
The ad says“Experience working with torch, high-level ML frameworks, and inference engines (vLLM or TensorRT).”
The ad says“Experience working with torch, high-level ML frameworks, and inference engines (vLLM or TensorRT).”
The ad says“Experience working with torch, high-level ML frameworks, and inference engines (vLLM or TensorRT).”

Values

The ad says“Nice-to-have: familiarity with low-level operating system foundations (Linux kernel, file systems, containers, etc).”
The ad says“Nice-to-have: familiarity with low-level operating system foundations (Linux kernel, file systems, containers, etc).”
The ad says“Nice-to-have: familiarity with low-level operating system foundations (Linux kernel, file systems, containers, etc).”

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 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).