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Runpod

Rents cloud GPUs for building AI applications

Technical Support Engineer

Customer SupportUpdated on Nov 7
Full-timeRemote in USA$73.2k - $97.6kRemote

About the Job

Requirements

  • At least 5 years of professional experience in technical customer support.
  • A bachelor's degree in a relevant field, such as Computer Science, Computer Engineering, Software Engineering, or Information Technology, or equivalent professional experience.
  • Familiarity with applied artificial intelligence use cases such as inference, fine-tuning, large language model-based applications, or agentic systems.
  • At least 2 years of experience in software engineering or development, or at least 2 years of experience as a network administrator.
  • Knowledge of operating systems including Linux and Ubuntu; containerization with Docker; SQL databases such as MySQL and PostgreSQL; data center components such as server hardware and network operations; and network protocols such as TCP/IP, HTTP/HTTPS, SSH, and DNS.
  • Ability to write and execute scripts, queries, or commands, and to read and understand code, logs, errors, or traces in Python.
  • Strong problem-solving ability and skill in analyzing and resolving technical issues.
  • Excellent verbal and written communication skills.
  • Advanced knowledge of operating systems, proficiency with SSH and terminal commands, and experience with Linux server versions.
  • Eligibility to work in the United States.

Responsibilities

  • Communicate clearly and promptly with customers about issue status and work to ensure customer satisfaction.
  • Provide support through email, phone, chat, and video calls, including Google Meet and Zoom.
  • Troubleshoot and resolve critical or complex escalated technical issues involving product or service configuration, performance, functionality, compatibility, or code errors.
  • Use code analysis, test scripting, log analysis, remote access, and other tools and methods to identify root causes and provide solutions or workarounds.
  • Communicate with customers and internal engineering, sales, supply, and product management teams to support customer satisfaction and incorporate feedback.
  • Assist the support team with escalated technical tickets, collaborate with engineering on workarounds or bug fixes, and work with infrastructure on GPU server-related issues.
  • Relay customer and support-team feedback to engineering to inform product development and testing and help shape product improvements.
  • Create and maintain technical documentation, including knowledge base articles, FAQs, guides, and manuals.
  • Develop and deliver training sessions, webinars, and demonstrations for customers, partners, and internal teams.
  • Use diagnostic tools and techniques to identify root causes and implement fixes, updates, or changes as needed.
  • Analyze software code and logs to identify bugs or performance issues and develop solutions or workarounds.
  • Analyze system performance, errors, and configurations to help maintain system stability and reliability.
  • Assist customers with software installation, setup, configuration, and customization.

Desired Qualifications

  • Proficiency with Datadog, Grafana, SQL databases, and customer relationship management tools.
  • Proficiency in Docker and deploying with Docker.
  • Proficiency in Django, Flask, PyTorch, JavaScript frameworks React and Node.js, and Go, with hands-on full-stack development experience.
  • Experience deploying and managing AI/ML models as services or APIs, and understanding their training, inference, and deployment processes on Runpod platforms.
  • Experience with AI fine-tuning or AI inference.

About the Company

Runpod

Rents cloud GPUs for building AI applications

Runpod rents cloud computing power to developers and businesses building AI applications, with GPUs for model training, fine-tuning, and running models for users. Its Pods give teams a dedicated environment to run their own containers, while Serverless turns code into endpoints that adjust capacity to incoming requests. Clusters link multiple GPUs for larger training jobs, and the platform includes tools to deploy and monitor workloads through a web console or software interfaces. Customers pay for metered compute time and storage, with options for reserved resources and enterprise support. Runpod was founded in 2022 and is headquartered in San Francisco, California.

Company size
51-200
Company stage
Growth Equity (Venture Capital)
Total funding
$122.3M
Headquarters
San Francisco, CA
Founded
2022

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