Head of AI Workflows / Lead Architect

Wonder Studios•Los Angeles, CA

1d•$150,000 - $200,000•Hybrid

About The Position

Wonder Studios is an AI-native film and creative studio redefining how stories are told. We partner with global brands, artists, and studios to create award winning campaigns, films, and experiences that merge cinematic craft with cutting-edge AI technology. We've raised more than $15 million from some of the world's best investors including LocalGlobe, Atomico and Adobe as well as angels from the leading AI companies including OpenAI, ElevenLabs, Google DeepMind. We're now a team of 35+ based around the world with offices in London, LA, NYC and SF. We need a data driven, creative and technical leader to turn rapidly evolving generative technology into practical techniques that help filmmakers make more ambitious work. This role sits across filmmakers, Wonder's go-to-market organization, model partners, the Wonder community, product, and engineering to turn emerging capabilities into production-ready workflows. This is a filmmaker-first role. The goal is not to replace directors, artists, or production teams; it is to give them new creative options, remove technical friction, and apply AI intentionally where it strengthens the story and the filmmaking process. You'll be responsible for developing and hardening our institutional knowledge base and working with engineering to turn this into proprietary technology. You'll work directly with Wonder's engineering leadership, studio partners, filmmakers, artists, and production teams to own meaningful workflow decisions from day one and help architect how Wonder creates (what tools we use and what blueprints we follow).

Requirements

Significant experience in VFX, post-production, animation, creative technology, or production engineering; a blend between an engineering first-principles mindset and a passion for film and creativity
Hands-on knowledge of generative and traditional VFX image and video systems, APIs, node-based workflows, and platforms such as ComfyUI, Stable Diffusion, Flux, Runway, Veo, Luma, Adobe Creative Cloud, DaVinci Resolve, Unreal Engine, Nuke, or Maya
A demonstrated track record of taking emerging technology from early evaluation and prototyping through to a dependable workflow used on a real production
Comfort working directly with directors, producers, production designers, VFX supervisors, editors, artists, engineers, model partners, customers, and senior executives
Strong technical and product judgment across creative quality, controllability, infrastructure cost, reliability, security, licensing, production deadlines, and operational risk
Excellent communication and teaching skills, with the ability to turn complex techniques into clear documentation, demonstrations, workshops, and reusable learning resources
High ownership, bias to action, intellectual honesty, and a filmmaker-first mindset — you move quickly, communicate limitations clearly, and believe AI should amplify creative teams rather than replace them

Responsibilities

Partner with Wonder's GTM team and leading generative model providers, including Google, Kling, ByteDance, Magnific, and others, to test early access to upcoming models through APIs, assess creative and production readiness, and identify where new capabilities can create value for filmmakers and partners
Work with the Wonder community to discover emerging techniques, identify workflows with real production potential, and develop the strongest experiments into repeatable studio capabilities
Build and maintain a shared library of AI filmmaking techniques, reference workflows, model evaluations, and production playbooks that equips creative technologists and AI production teams across Wonder and its community
Read and break down scripts with filmmakers to identify scenes, sequences, and production challenges where AI can expand creative options, accelerate iteration, or solve practical constraints, always in service of the filmmaker's intent
Turn proven techniques into dependable production workflows by leading prototypes and deployments, integrating established creative tools and infrastructure, defining quality, cost, and reliability benchmarks, and translating recurring needs into product and engineering priorities