Head of Technology - Ink

Netflix Netflix · Big Tech · Los Angeles, CA +2 · Inkubator

Head of Technology for a GenAI-native animation studio, responsible for the end-to-end technology stack including artist tools, production workflows, infrastructure, security, and data platforms. This role involves defining technology strategy, leading engineering teams, enabling creative ambition through AI, and scaling operations for multiple productions. Key areas include generative workflows, asset management, inference/training technologies, data pipelines for model fine-tuning, and secure AI usage.

What you'd actually do

  1. Work with INK leadership to define and communicate INK’s long-term technology strategy, with a focus on GenAI-enabled workflows, artist tooling, and scalable, secure multi-show environments.
  2. Partner with INK and Netflix leadership to align technology roadmaps with the studio’s creative, production, and operational goals.
  3. Provide technical leadership for INK’s end-to-end production pipeline, including: Generative workflows, Asset and material generation, management, and standards, Production tracking, review, and editorial support tooling, Real-time, 2D, CG and hybrid pipelines used in partnership with GenAI technology
  4. Lead evaluation and selection of tools, models and workflows, and ensuring they are robust enough and customizable for production– partnering with Netflix engineering to leverage internal solutions where relevant, and align on standards
  5. Guide research into: Scalable inference and training technologies, Data pipelines and standards for training and fine-tuning models (including LoRA-based approaches), Best practices for sourcing and generating visual data using traditional pipelines, real-time engines, video capture, and 2D animation.

Skills

Required

  • 12+ years of experience in technology roles within animation, VFX, games, or closely related digital content industries, including significant time in pipeline, tools, or infrastructure leadership.
  • 7+ years of management and strategic leadership experience overseeing multidisciplinary engineering/technical teams, ideally across multiple concurrent productions or locations.
  • Proven experience designing and operating end-to-end content pipelines (animation, VFX, or real-time) that intersect with: Distributed compute, Scalable storage and data management, Production tracking and review systems, Artist- and show-facing tooling and APIs
  • Demonstrated understanding of Generative AI or ML-driven workflows (e.g., image/video generation, style transfer, asset generation, LLM- or LoRA-based customization) and the associated data and infrastructure requirements.
  • Strong understanding of building and maintaining secure, multi-tenant environments for multiple shows, including IAM, environment isolation, and compliance considerations.

Nice to have

  • Familiarity with modern and emerging software development technologies and AI-assisted coding tools (e.g., Claude Code, GitHub Copilot, internal Netflix solutions), and how to integrate them into scalable studio workflows.
  • Track record of partnering directly with creative leaders (Directors, Pr

What the JD emphasized

  • GenAI-native animation studio
  • end-to-end technology stack
  • GenAI-enabled workflows
  • Generative AI
  • scalable inference and training technologies
  • Data pipelines and standards for training and fine-tuning models
  • Generative AI or ML-driven workflows

Other signals

  • GenAI-native animation studio
  • end-to-end technology stack
  • Generative AI-enabled workflows
  • artist tooling
  • scalable, secure multi-show environments
  • rapid experimentation with new and emerging technologies (including Generative AI)
  • scale to support multiple concurrent productions
  • Generative workflows
  • Asset and material generation, management, and standards
  • Real-time, 2D, CG and hybrid pipelines used in partnership with GenAI technology
  • evaluation and selection of tools, models and workflows
  • scalable inference and training technologies
  • Data pipelines and standards for training and fine-tuning models (including LoRA-based approaches)
  • sourcing and generating visual data
  • ethical, secure AI usage
  • Distributed compute for artist workstations and scalable training/inference workloads
  • Storage, asset management, and data pipelines across shows
  • Generative AI or ML-driven workflows (e.g., image/video generation, style transfer, asset generation, LLM- or LoRA-based customization)