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VESSL AI

GPU cloud platform for AI workloads

Senior Backend Engineer

Backend EngineeringConfirmed live in the last 24 hours
Full-timeSeoul, South KoreaNo salary listedHybrid

About the Job

Requirements

  • A bachelor's degree or higher in computer science, electrical engineering, AI, or a related major, or equivalent practical experience.
  • At least 5 years of software engineering experience.
  • Experience designing, developing, and operating production services using languages such as Go or Python.
  • Experience designing and operating production systems based on Kubernetes.
  • Understanding of Linux systems and troubleshooting experience.
  • Experience leading system design and technical decisions across multiple teams.

Responsibilities

  • Design and implement a GPU cloud control plane, including API servers, cluster agents, and billing systems, using Go on multi-cluster Kubernetes.
  • Develop an orchestration system to manage provisioning, scheduling, and lifecycle of container workspaces and GPU virtual machines on a unified platform.
  • Improve platform performance bottlenecks, including GPU and network performance in virtualized environments through PCI passthrough, and cache hit rates and performance in inference services.
  • Automate GPU cluster operations, including node health detection, recovery, and mode transitions, and use failure experience to improve platform reliability.
  • Establish team technical standards and raise engineering quality through design reviews, code reviews, and mentoring.

Desired Qualifications

  • Experience developing systems with Kubernetes controllers or operators and client-go.
  • Experience with virtualization technologies such as KubeVirt or KVM, or performance analysis and optimization in virtualized environments.
  • Experience operating GPU-based training and inference workloads as services.
  • Experience deploying, operating, or tuning LLM serving engines such as vLLM or SGLang.
  • Understanding of LLM inference optimization techniques such as KV cache, prefill/decode disaggregation, and quantization.
  • Experience developing high-traffic systems such as API gateways and usage metering.
  • Experience owning and evolving core production systems over an extended period.
  • Experience mentoring junior engineers and establishing team technical standards.

About the Company

VESSL AI

GPU cloud platform for AI workloads

What VESSL AI does: It runs VESSL Cloud, a GPU cloud platform built for AI workloads. It gives teams on-demand access to high-end GPUs (A100, H100, B200, B300, GB200) to run activities like LLM fine-tuning, large-scale model training, and AI inference. How it works: Users launch GPU instances in VESSL Cloud from VESSL’s own data center infrastructure, paying per minute for usage, and using the platform to accelerate AI development without managing physical hardware. How it stands out: It operates its own data center and offers per-minute billing, delivering up to 80% cost savings versus AWS and GCP, focusing specifically on AI workloads rather than general-purpose cloud services. What the goal is: To help AI teams move quickly from idea to production by removing infrastructure overhead and providing scalable, affordable GPU resources for AI work.

Company size
51-200
Company stage
Series A
Total funding
$16.6M
Headquarters
San Francisco, CA
Founded
2020

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