Responsibilities

• Own Runpod's developer relations strategy: define the programs, content, and engagement motions that drive developer adoption, education, and advocacy
• Create high-quality technical content: tutorials, guides, sample applications, benchmarks, integration walkthroughs, and video demos that help developers succeed on the Runpod platform
• Build and maintain reference architectures, sample repos, and integration examples that demonstrate Runpod's capabilities across inference, training, fine-tuning, and agent workloads
• Represent Runpod at developer conferences, meetups, and community events (AI/ML conferences, KubeCon, NVIDIA GTC, PyCon, etc.); deliver technical talks and workshops
• Engage actively in the open-source AI ecosystem: contribute to relevant projects, build integrations with popular frameworks (PyTorch, Hugging Face Transformers, vLLM, LangChain, etc.), and cultivate relationships with maintainers
• Build relationships with AI/ML influencers, YouTubers, technical bloggers, and educators who reach Runpod's target audience; support and enable their content creation efforts
• Collaborate with Content and Marketing to align technical content with product launches, campaigns, and positioning
• Contribute to Runpod's documentation and developer experience by identifying gaps, writing docs, and working with Product to improve onboarding flows

Requirements

• 5+ years of combined experience in developer relations, developer advocacy, community management, or a hybrid role at a developer-focused company
• Strong technical skills: you can write production-quality code (Python required; Go, Rust, or TypeScript a plus), build demos, and debug developer issues in real time
• Demonstrated ability to create compelling technical content: blog posts, tutorials, conference talks, video content, or open-source contributions with measurable reach
• Deep familiarity with the AI/ML ecosystem: model training and inference workflows, popular frameworks (PyTorch, Hugging Face Transformers, vLLM), containerized deployments, and GPU compute concepts
• Deep fluency with Discord as a community platform, including bots, roles, channel architecture, and moderation tooling; experience managing communities of 10K+ members
• Experience building community or developer programs from scratch (ambassador programs, hackathons, user groups, content pipelines) with measurable results
• Strong public speaking skills with a track record of delivering technical talks at conferences, meetups, or webinars
• Strong written communication with the ability to move between developer language and business context; equally comfortable writing a deployment tutorial and a product feedback summary
• Experience working cross-functionally with Product, Engineering, and Marketing teams in a fast-moving environment
• Comfort operating in a remote-first environment with minimal structure and high autonomy
• Successful completion of a background check

Preferred

• Experience at a cloud infrastructure, GPU compute, or developer tools company (AWS, GCP, Cloudflare, Lambda, CoreWeave, Modal, Replicate, Hugging Face, Vercel, etc.)
• Hands-on experience deploying and optimizing ML models in production: inference optimization, cold start reduction, model serving, or distributed training
• Existing presence and following in the AI/ML developer community (Twitter/X, YouTube, blog, GitHub)
• Experience managing or contributing to open-source communities
• Familiarity with serverless compute, container orchestration (Kubernetes, Docker), and cloud-native deployment patterns
• Track record of building community-sourced content pipelines that feed into marketing or documentation efforts
• Experience at a Series A through C stage company where you built DevRel or community from early stage

Benefits

• Generous medical, dental & vision plans
• Flexible PTO- take the time you need to recharge
• Most roles are remote work first with an inclusive, collaborative teams utilizing slack as the main form of internal communication
• Join a passionate team on the cutting edge of AI infrastructure — where culture, learning, and ownership are at the heart of how we scale.