Distributed Systems Engineer — Scale GPU Cloud Platforms

Beam; New York (NY); On-site; USD 120,000 - 170,000; Full time

Benefits offered by this job

Competitive salary

Equity

Health, dental, vision

Learning budget

Fitness stipend

Job summary

Beam is seeking a Platform Engineer to advance our ultrafast AI inference platform. You will work on low‑level systems, container runtimes, OCI image formats, and lazy‑loading large files from content addressable storage, optimizing GPU utilization across clouds.

Our backend is primarily Go, with Python components. You’ll collaborate closely with customers, scale workloads to thousands of GPUs, and contribute to cutting‑edge features like GPU checkpoint restore and CRIU.

Qualifications

• 3–5 years of experience working with a large distributed system.
• Proficient with Kubernetes and cloud-native architectures.
• Experience writing a statically typed language (Go or Rust).
• Familiar with gRPC, Helm/Kustomize, and Terraform.
• Comfortable collaborating with customers and open-source communities.

Responsibilities

• Work on low-level systems, container runtimes, OCI image formats, and lazy-loading large files from content-addressable storage.
• Efficiently pack thousands of workloads into GPUs across multiple clouds.
• Contribute to cutting-edge GPU features like checkpoint/restore and CRIU.

Skills

Distributed systems

Kubernetes

Go

Rust

gRPC

Helm/Kustomize

Terraform

Customer collaboration

Cloud native

Open source

Tools

Container runtimes

Job description

Beam is seeking a Platform Engineer to advance our ultrafast AI inference platform. You will work on low‑level systems, container runtimes, OCI image formats, and lazy‑loading large files from content addressable storage, optimizing GPU utilization across clouds.

Our backend is primarily Go, with Python components. You’ll collaborate closely with customers, scale workloads to thousands of GPUs, and contribute to cutting‑edge features like GPU checkpoint restore and CRIU.