We are looking for a Senior Software Engineer to design and build tools, pipelines, and AI-enabled systems for complex creative production workflows. You will connect AI models with DCC applications, APIs, internal services, and production infrastructure to support artists, technical artists, designers, and engineers.
A central part of the role is building reliable, multi-step agentic workflows that coordinate tools, maintain state, operate within defined permissions, and recover from failures. You will also develop artist-facing applications and content pipeline services, combining hands-on engineering with technical leadership to deliver maintainable, production-quality systems.
What you’ll be doing:
- Own systems from workflow discovery and architecture through implementation, rollout, maintenance, and production support.
- Build agentic workflows that coordinate models, tools, APIs, and services, including context and state management, retries, partial failure handling, and human review.
- Develop AI-enabled engineering tools for coding, testing, debugging, codebase analysis, code review, and validation.
- Create DCC integrations, plugins, panels, desktop applications, and pipeline services that address artist and technical artist workflows.
- Build and evolve content pipelines covering authoring, validation, publishing, versioning, asset management, and delivery to rendering or real-time systems.
- Define APIs and data contracts, and manage dependencies, packaging, compatibility, and releases across production environments.
- Establish automated testing, agent evaluations, tracing, monitoring, and performance measurements, alongside access controls, approval points, and recovery mechanisms.
- Work directly with users to identify problems and prioritize improvements; document systems, support adoption, mentor engineers, and lead technical reviews.
What we need to see:
- Bachelor’s degree in Computer Science, Computer Engineering, or a related technical field, or equivalent practical experience.
- 8+ years of relevant software engineering experience building and supporting production tools, content pipelines, or comparable workflow systems.
- 2+ years of technical leadership experience, including leading projects, guiding architectural decisions, mentoring engineers, and coordinating delivery.
- Strong Python skills and sound judgment in software architecture, API design, testing, debugging, and maintainable code.
- Experience integrating with DCC applications and supporting content workflows from authoring through validation, publishing, and downstream delivery.
- Experience building multi-step AI workflows that invoke tools or APIs and interact with existing software systems, including state management, validation, failure handling, and evaluation.
- Ability to develop usable tools for artists or other specialist users and translate workflow needs into practical engineering requirements.
- Experience with version control, code review, automated testing, and release practices appropriate to production-critical software.
- Ability to diagnose issues across application, service, and data boundaries, communicate technical tradeoffs, and collaborate across engineering and creative teams.
Ways to stand out from the crowd:
- Background with Houdini, Maya, Blender, or Nuke extension APIs and artist-facing interfaces built with PySide/Qt or similar frameworks.
- Experience with USD/OpenUSD, asset publishing, production tracking, asset management, and Perforce-based content workflows.
- Proficiency in C++ or Java, or experience with Unreal Engine, Unity, proprietary engines, graphics, rendering, or shader development.
- Experience with LLM APIs, tool calling, structured outputs, retrieval and context systems, and agent evaluations measuring quality, latency, and cost.
- Experience with distributed services, job scheduling, asynchronous workflows, caching, or large-scale processing.
Artists and engineers adopt tools that reduce manual work and solve recurring production problems. AI workflows complete useful tasks with measurable quality, traceable execution, and predictable failure handling. Content moves reliably through validation, publishing, and downstream integration, while systems remain maintainable as applications, models, and production requirements evolve.
You will work across the full production workflow, from artist-facing tools to backend services and AI orchestration, and see your engineering decisions affect how content is created and delivered. The opportunity is to extend established production systems with AI capabilities that perform useful work under real production constraints.