Staff ML Infrastructure Engineer

2 days ago

Hayward CA, United States Cubiq Recruitment Full time

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.

Core ML Platform Architecture: Design and evolve the infrastructure that supports large-scale generative video and multimodal model training, evaluation, and deployment.
Build and optimize GPU/TPU clusters, distributed training systems, and orchestration layers tailored for video-heavy pipelines.
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.

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.
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.

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.

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