We9re looking for a Technical Creative Director, Generative Imagery Systems, to lead work at the intersection of generative AI, visual craft, and product storytelling. This role will help define what AI-made visual media looks like when it9s held to Apple9s standard of quality, intentionality, and humanity.
You9ll shape both the what and the how of generative visuals at Apple, working alongside Design, ML/AI, Marketing, and Engineering teams, across photorealism, illustration, motion, texture, spatial media, and formats that don9t exist yet. You come from a technical or computational background but think like an artist and communicate like a creative leader. You thrive in deeply collaborative environments where the best outcome comes from combining perspectives, not working in isolation. This is a rare opportunity to help define a creative discipline at the moment it9s being born, inside a company where craft and intentionality aren9t aspirations but requirements.
Description
As Technical Creative Director of Generative Imagery Systems, you will:
Co-develop the creative direction for how generative AI produces imagery worthy of Apple9s in-product experience, working with Design and Product teams to align on visual modes, styles, and quality benchmarks
Lead and collaborate with generative artists, creative technologists, tool developers, and generative systems designers across projects that require tight cross-functional coordination
Partner with Engineering and tool development teams to build creative pipelines that allow generative work to be produced at scale with consistency, quality control, and stylistic precision
Co-create evaluation frameworks with Design, ML/AI, and QA partners that codify what 49good49 looks like for AI-generated imagery, and build shared systems to measure and enforce it
Explore new formats and applications alongside product and platform teams, charting where generative imagery goes next across spatial computing, interactive experiences, and emerging platforms
Collaborate with policy, legal, and ethics partners to develop standards for responsible generative creative, including provenance, bias, and representation
Align generative creative strategy with teams across Design, ML/AI, Marketing, and Engineering through ongoing partnership and shared planning
Work with sibling content teams to build bridges between sourced, captured, and generated imagery, ensuring a coherent creative ecosystem
Minimum Qualifications
Deep, hands-on fluency with generative models (diffusion, transformer-based synthesis, procedural systems, or related architectures) and practical understanding of how model architecture, training data, and parameter choices manifest in visual outcomes
Experience building or directing creative tools, custom pipelines, or production workflows that harness ML/AI for visual output at scale, including control mechanisms, fine-tuning methods, and reproducibility infrastructure
Knowledge of inference and deployment considerations: compute tradeoffs, optimization techniques, and how they constrain or enable creative decisions
Experience designing evaluation systems for generated imagery, whether quantitative metrics, structured human review, or both
A refined visual eye and a body of work that demonstrates taste, craft, and a point of view, in any format (portfolio, generative art, research, tools, experimental projects)
Experience building teams or practices from zero in ambiguous, emerging spaces, with a track record of bringing cross-functional partners along from the start
Ability to translate technical possibilities into creative direction that non-technical partners can rally around and contribute to
A deep sense of responsibility about the ethical, cultural, and societal implications of generated imagery
Preferred Qualifications
A point of view on where generative imagery is headed in 3-5 years, and the conviction to make bets
Background in adjacent fields: VFX pipelines, computational photography, real-time rendering, creative coding, or technical art direction
Direct experience training models or contributing to model development, not just using existing tools
Understanding of data provenance, rights-cleared training, and responsible model infrastructure
Exposure to emerging areas: video generation at scale, 3D asset generation, spatial/volumetric content, or audio-visual synthesis
A history of thriving in matrixed, cross-functional environments where influence matters more than authority
