Livepeer · 12 hours ago

Senior Developer Advocate - Video AI (Remote)

Brooklyn, NY

Full-time

Remote

Senior Level

5+ years exp

Maximize your interview chances

CommunitiesInformation Technology

Insider Connection @Livepeer

Discover valuable connections within the company who might provide insights and potential referrals.
Get 3x more responses when you reach out via email instead of LinkedIn.

Responsibilities

Building Easy-To-Copy Demos

Experiment with the latest AI/ML magic to create interesting new workflows (pipelines) on ComfyUI and beyond.

Dream up and code small, eye-catching projects that show off Livepeer’s superpowers in real-time AI video.

Craft open-source tools, SDKs, and libraries to make life easier (and more fun) for developers diving into Livepeer.

Engage a community of worldwide developers —answer their questions, gather their ideas, and build something amazing together.

Host and join hackathons, meetups, and events where creativity meets innovation (bonus points if snacks are involved).

Be the ultimate developer cheerleader, ensuring their feedback shapes Livepeer’s future and makes them feel heard.

Produce technical tutorials, guides, and blog posts to educate developers on building video AI applications using Livepeer.

Create engaging video demos and walkthroughs to visually demonstrate Livepeer’s capabilities.

Host webinars and workshops to teach best practices and inspire creative uses of Livepeer technology.

Qualification

Find out how your skills align with this job's requirements. If anything seems off, you can easily click on the tags to select or unselect skills to reflect your actual expertise.

Developer AdvocacyAI/ML ProficiencyVideo TechnologyComfy UICloud DeploymentPerformance OptimizationComputer VisionWorkflow DevelopmentModular PipelinesWebinarsOpen-source ToolsDockerKubernetesTensorFlowPyTorchHugging FaceOpenCVFFmpegGStreamerTechnical WritingPresentations

Required

5–6 years of relevant experience.

Solid track record in developer advocacy / evangelism, solutions engineering, or hands-on experience in video technology, AI/ML, or building tools that developers love.

Proficient in creating modular node graphs and workflow modularization, with custom node development in Python and experience in visual tools like TouchDesigner and Grasshopper.

Active and engaged Twitter following, with a track record of sharing insightful content, fostering discussions, and building connections within the developer, AI, or video technology communities.

Proven track record of fostering developer communities, driving engagement through various channels.

Comfortable delivering presentations and workshops to both technical and non-technical audiences. Experience giving presentations both virtually and IRL.

Actively shares workflow designs, documents custom nodes, maintains repositories, produces tutorials, and emphasizes clear attribution and licensing for collaborative development.

Good understanding of diffusion models, LoRA training, model merging, ONNX optimization, and prompt engineering, alongside expertise in TensorFlow, PyTorch, and Hugging Face for AI/ML development and integration.

Experienced in deploying scalable AI workloads on AWS, GCP, Azure, NVIDIA Jetson, and containerized environments using Docker and Kubernetes, with a focus on real-time, low-latency solutions.

Skilled in computer vision and video-specific AI models (e.g., Stable Diffusion, StyleGAN) with a focus on optimizing inference for real-time performance using GPUs, TPUs, and FPGAs.

Skilled in GPU memory management, model pruning, quantization, batch processing, and VRAM optimization, with advanced knowledge of hardware acceleration techniques and scaling for high-concurrency, low-latency applications.

Proficient with video processing tools like OpenCV, FFmpeg, and GStreamer, familiar with codecs, frame extraction/reconstruction, and real-time video transformation.

Competent in infrastructure-as-code and CI/CD pipelines for deploying AI models into production, managing data flows, and ensuring seamless scalability with robust error handling.

Strong preference for pre-built libraries, plug-and-play functionality, and robust APIs/SDKs for rapid experimentation and integration with cloud and edge platforms.