Member of Technical Staff - ML Performance

Modal

New York, NYFull-time$150–350K/yrPosted 1y agoStill listed 3 days ago

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At a glance

Compensation

$150–350K/yr

Location

New York, NY

Schedule

Full-time

Work Authorization

Not specified

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Job overview

Modal is hiring a Member of Technical Staff - ML Performance. Modal is building a new AI infrastructure layer that enables low‑latency inference, fine‑tuning, and scalable model serving. The company serves high‑profile customers such as Lovable, Ramp, Cognition, DoorDash, and Suno, offering instant GPU access, sub‑second container starts, and native storage. The role seeks engineers who can improve ML system performance at scale, contribute to open‑source projects, and enhance Modal’s container runtime for higher throughput and lower latency.

Successful candidates bring 5+ Years Experience Writing High-Quality, High-Performance Code. Important skills include PyTorch, VLLM, TensorRT, Nvidia GPU Architecture, CUDA, and ML Performance Engineering. Preferred (not required): Low-Level Operating System Foundations, Linux Kernel, File Systems, and Containers.

Skills & qualifications

RequiredNice to have

Skills

PyTorchVLLMTensorRTNvidia GPU ArchitectureCUDAML Performance EngineeringDebugging SM Occupancy IssuesRewriting Algorithm to Be Compute-BoundEliminating Host OverheadMaking ML Systems Performant at ScaleContributing to Open-Source ProjectsPush Language and Diffusion Models Towards Higher Throughput and Lower LatencyHigh-Level ML FrameworksInference EnginesLow-Level Operating System FoundationsLinux KernelFile SystemsContainers

Qualifications

5+ Years Experience Writing High-Quality, High-Performance Code

Full 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 Lovable https://modal.com/blog/lovable-case-study, Ramp https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal, 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 https://modal.com/blog/modal-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 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).

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