RunPod

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About
RunPod is a cloud platform that provides GPU instances for AI/ML workloads. It offers various options like Secure Cloud and Community Cloud, On-Demand and Spot Pods. Users can deploy any container and scale ML inference with serverless options. RunPod also offers network storage and autoscaling features.
Platform
Task
Features
• serverless gpu
• network storage
• gpu cloud
• secure cloud
• spot pods
• on-demand pods
• community cloud
• pods
FAQs
Secure Cloud vs Community Cloud
RunPod provides two cloud computing services: Secure Cloud and Community Cloud.
On-Demand vs. Spot Pod
On-Demand Pods can run forever without interruptions with resources dedicated to your Pod. They do incur higher costs than Spot Pods.
Where can I go for help?
We'd be happy to help! Join our community on Discord, message us in our support chat, or email us at help@runpod.io.
What is an On-Demand instance?
On-Demand instances are for non-interruptible workloads. You pay the On-Demand price and cannot be displaced by other users if you have funds to keep your Pod running.
What is a Spot instance?
A Spot instance is an interruptible instance that can generally be rented much cheaper than an On-Demand one. Spot instances are great for stateless workloads like an API or for workloads you can periodically save to a volume disk.
What is a Savings Plan?
Savings Plans are a way for you to pay up-front and get a discount for it. This is great for when you know you will need prolonged access to compute. You can learn more on the about Savings Plans here
Is my data protected from other clients?
Yes. Your data is run in a multi-tenant environment where other clients can't access your pod. For sensitive workloads requiring the best security, please use Secure Cloud.
Is my data protected from the host of the machine my Pod is running on?
Data privacy is important to us at RunPod. Our Terms of Service prohibit hosts from trying to inspect your Pod data or usage patterns in any way. If you want the highest level of security, use Secure Cloud.
What can I do in a RunPod Pod?
You can run any Docker container available on any publicly reachable container registry. If you are not well versed in containers, we recommend sticking with the default run templates like our RunPod PyTorch template. However, if you know what you are doing, you can do a lot more!
What if it says that my spending limit has been exceeded?
We implement a spending limit for newer accounts that will grow over time. This is because we have found that sometimes scammers try to interfere with the natural workings of the platform.
Do you have some legal stuff I can look at?
Sure, do! Take a look at our legal page
Is RunPod compliant with GDPR for data processed in Europe?
Yes, RunPod is fully compliant with the General Data Protection Regulation (GDPR) requirements for any data processed within our European data center regions.
Pricing Plans
Job Opportunities
Cloud Software Engineer
RunPod offers GPU cloud computing for AI/ML, providing secure and community cloud options, on-demand and spot pods, and serverless GPU scaling.
Benefits:
Stock options
The flexibility of remote work with an inclusive, collaborative team.
An opportunity to grow with a company that values innovation and user-centric design.
Generous vacation policy to ensure work-life harmony and well-being.
Contribute to a company with a global impact based in the US, Canada, and Europe.
Education Requirements:
Bachelor’s degree in Computer Science, Computer Engineering or a related field, or equivalent experience.
Experience Requirements:
3+ years of professional experience in software development, with experience in Go.
Strong understanding of the Go programming language and its ecosystem.
Strong problem-solving skills and ability to work in a collaborative environment.
Excellent communication skills and attention to detail.
Successful completion of a background check
Responsibilities:
Design, develop, and maintain cloud infrastructure software, primarily in Go.
Collaborate with managers and other engineers to define and implement product requirements.
Troubleshoot and optimize existing code to improve performance and reliability.
Participate in code reviews and contribute to the team's technical standards.
Contribute to architectural discussions and decisions.
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Forward Deployed Engineer
RunPod offers GPU cloud computing for AI/ML, providing secure and community cloud options, on-demand and spot pods, and serverless GPU scaling.
Benefits:
Stock options
Commission opportunity
The flexibility of remote work with an inclusive, collaborative team.
An opportunity to grow with a company that values innovation and user-centric design.
Generous vacation policy to ensure work-life harmony and well-being.
Experience Requirements:
Proficient in deploying and managing AI/ML models as service, with a strong understanding of training, fine-tuning, inference, and deployment.
3+ years of software development experience in JavaScript, Go (Golang), or Python.
Strong problem-solving skills and ability to work in a collaborative environment.
Excellent communication skills and attention to detail.
Successful completion of a background check
Responsibilities:
Troubleshoot and resolve critical or complex technical issues escalated by customers, including those related to configuration, performance, functionality, compatibility, or code errors in RunPod’s products or services.
Utilize various tools and methods, such as code analysis, scripting for testing, log analysis and remote access, to identify root causes and deliver solutions or workarounds.
Communicate effectively with customers and internal teams, including engineering, sales, and product management teams, to ensure customer satisfaction and integrate valuable feedback.
Participate in sales meetings with customers, explain RunPod’s specific technologies, provide architectural recommendations, and build proof-of-concept solutions to support onboarding for potential high-spending customers.
Assist the support team in troubleshooting and resolving escalated technical tickets, collaborate with the engineering team to provide workarounds or bug fixes, and work with the infrastructure team to address GPU server-related issues.
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Security Engineer
RunPod offers GPU cloud computing for AI/ML, providing secure and community cloud options, on-demand and spot pods, and serverless GPU scaling.
Benefits:
Stock options
The flexibility of remote work with an inclusive, collaborative team.
An opportunity to grow with a company that values innovation and user-centric design.
Generous vacation policy to ensure work-life harmony and well-being.
Contribute to a company with a global impact based in the US, Canada, and Europe.
Education Requirements:
Bachelor's degree in Computer Science, Cybersecurity, or a related field
Experience Requirements:
5+ years of experience in information security roles, with a focus on cloud security
Strong programming skills in at least one language (ideally, Python, Go, or C)
Extensive knowledge of Linux kernel internals, containerization technologies, and virtualization
Deep understanding of workload/network isolation techniques in multitenant cloud environments
Experience securing and hardening cloud infrastructure, particularly in environments with untrusted workloads
Responsibilities:
Design and implement secure architectures for RunPod's multitenant GPU cloud platform, ensuring strong isolation between customer workloads
Conduct thorough security assessments, including threat modeling, code reviews, and penetration testing of our cloud infrastructure and services
Develop and implement security fixes and improvements in collaboration with software engineering teams
Implement and manage security tools and systems (e.g., SIEM, WAF, EDR)
Create and maintain security documentation, including policies, procedures, and technical guidelines specific to GPU cloud security
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