Founding Engineer, Platform / AI Systems

The roleGengis AI is building applied AI for the operational layer of film, television, and media production, the hard work that happens after generation: turning creative material into structured workflows that production teams can review, refine, approve, and use. We are not building a foundation model. We are building a model-agnostic platform that makes AI useful inside real production environments. You will own the core systems, AI orchestration, evaluation workflows, and product infrastructure that make this possible. You will work directly with the founders and early production users to ship the first durable version of the platform. In the first phase, your focus will be a reliable workflow that helps production teams move from creative input to practical planning and visual development outputs. This is not a narrow backend role and not a pure ML research role; you should be able to reason across systems, product, applied AI, infrastructure, data, and user-facing execution.What you'll doCore platform architecture and backend systemsAI orchestration across text, image, and future video workflowsReliable async workflows for long-running AI and production tasksEvaluation systems that make output quality measurable, repeatable, and improvableHuman-in-the-loop review flows where users can refine, approve, reject, and iterateCost, reliability, and performance controls for AI-powered workflowsData handling, permissions, auditability, and security fundamentalsCloud deployment, CI/CD, observability, and operational disciplineEngineering standards and early technical hiring as the team growsWhat we're looking for7+ years building and shipping production software, with real ownership of systems taken from zero to live useStrong backend and systems architecture ability: APIs, databases, stateful services, async jobs, queues, and cloud infrastructureExperience building data-heavy, workflow-driven, or human-in-the-loop productsExperience building with LLMs, generative AI APIs, diffusion/video models, or AI orchestration systems in production or near-production environmentsPractical judgment around model failure modes, evaluation, cost, latency, reliability, and user trustComfortable designing evaluation workflows and quality metrics, not just shipping demo promptsFull-stack enough to ship end-to-end early, when the team is smallCloud and infra fluency: deployment, CI/CD, inference basics, observability, and security fundamentalsClear communication with product, creative, and non-technical usersAbility to set technical direction, make tradeoffs, hire well, and lead without over-engineeringNice to haveExperience with AI evaluation, model routing, provider abstraction, or cost/performance benchmarkingExperience with MLOps or data pipelines: versioning, reproducible evaluation, and dataset governanceExperience with media, film, creative tooling, workflow platforms, or collaborative review toolsExperience with Postgres, cloud GPU workflows, or event-driven infrastructureEarly-stage startup experience where you built the first durable version of a productYou'll thrive here ifYou want to own the technical layer that turns a strong vision into a working platformYou build for professionals who need AI to be useful, controllable, reliable, and commercially practical, not just impressive in a demoYou can ship the core system largely hands-on while setting architecture for the team that followsYou make pragmatic tradeoffs between speed, quality, cost, and runwayYou want senior ownership from day one, with a path toward Head of Engineering as the company scalesHow to applySend your application to [email protected] and include:CV and links to shipped work or codeA short note on one complex system you built or owned: what was hard, what you measured, what tradeoffs you made, and what you would do differently now