Staff ML Infrastructure Engineer
4 days ago
Santa Rosa
Staff / Lead ML Infrastructure Engineer San Francisco, CA — Onsite Salary - Over market average + equity We are building one of the world’s leading generative video and multimodal AI platforms, and we’re looking for a senior infrastructure engineer to drive the backbone that makes it possible. This role is ideal for an engineer from a top-tier tech company who has built cloud-scale systems, high-performance compute platforms, and battle-tested CI/CD pipelines that support complex ML workloads. What You’ll Own • Core ML Platform Architecture: Design and evolve the infrastructure that supports large-scale generative video and multimodal model training, evaluation, and deployment., • High-Throughput Compute Systems: Build and optimize GPU/TPU clusters, distributed training systems, and orchestration layers tailored for video-heavy pipelines., • Production Reliability for Generative Models: Create the tooling and services needed to safely push frequent model updates while handling massive compute loads and long-running jobs., • End-to-End CI/CD for ML: Lead the development of automated pipelines for model training, validation, artifact management, and production rollout., • Multimodal Data Infrastructure: Build systems to ingest, version, transform, and serve large-scale video, audio, and text datasets with high reliability., • Internal Developer Experience: Partner with research, product, and applied ML teams to build intuitive internal tooling for experiment tracking, model lineage, and resource scheduling., • Technical Leadership: Mentor engineers, set platform standards, and influence long-term architectural direction. What You’ve Done • Experience architecting and operating large-scale infrastructure at a cloud provider, hyperscaler, or leading AI company., • Built or owned mission-critical CI/CD systems, high-capacity compute platforms, or data infrastructure supporting ML teams., • Deep experience with distributed compute across GPUs/accelerators, Kubernetes, and cloud infrastructure (AWS/GCP/Azure)., • Strong engineering fundamentals in Python, Go, or equivalent languages., • Previous exposure to ML training pipelines—especially systems that handle heavy video, multimodal, or high-dimensional data., • Demonstrated ability to lead complex cross-org initiatives and drive technical strategy. Nice to Have • Experience with video processing systems, large-scale media pipelines, or streaming architectures., • Familiarity with modern multimodal or video-generation frameworks (PyTorch, JAX, diffusers, custom accelerators)., • Experience with Ray, Triton, CUDA optimization, or specialized scheduling for ML workloads., • Background working in high-growth AI startups or research-focused environments., • Security and compliance considerations for models that generate or process user content. Why Join • Shape the underlying platform powering one of the most advanced generative video systems in the world., • Influence the future of multimodal AI by building infrastructure that directly accelerates research and product breakthroughs., • Work closely with experienced founding engineers, researchers, and platform builders from leading tech companies., • Highly competitive compensation, meaningful equity, and strong in-person engineering culture in San Francisco.