About Luma AI
Luma's mission is to build multimodal AI to expand human imagination and capabilities. We believe that multimodality is critical for intelligence. To go beyond language models and build more aware, capable, and useful systems, the next step function change will come from vision. So, we are working on training and scaling up multimodal foundation models for systems that can see and understand, show and explain, and eventually interact with our world to effect change.
About the Role
Luma is pushing the boundaries of generative AI, building tools that redefine how visual content is created. We’re seeking an Agent Behavior Designer to help shape the logic, personality, and execution of Luma Agents within our core canvas platform.
This is an emerging and highly specialized discipline. While this role does not require shipping traditional full-stack production code, it extends far beyond standard prompt engineering. You will operate at the forefront of agentic engineering, working closely with the agent's core behavior, controlling its logic through sophisticated prompting stacks, and tracing causality across complex codebases and execution logs. You will define optimal user experiences, rigorously test agent behavior to identify suboptimal patterns, and iteratively refine complex prompt stacks to resolve them.
The ideal candidate demonstrates meticulous attention to linguistic nuance, possesses the technical proficiency to build Python evaluation harnesses, and exhibits the analytical rigor to iteratively resolve complex behavioral challenges.
What You'll Do
- Diagnose & Debug: Analyze complex agent traces and system logs to identify the root causes of suboptimal outputs. Trace causality through non-standardized text and prompts to resolve unexpected agent behaviors.
- Shape Agent Logic: Architect and refine prompt stacks to align agent behavior with intended outcomes. Oversee context window management and implement tool-calling best practices to ensure optimal agent functionality.
- Build Evals & Ground Truth: Develop Python-based scripts and evaluation harnesses for robust quantitative and qualitative testing. Partner collaboratively with the Evals team to define performance baselines and measurement frameworks.
- Understand and Advocate for the User Experience: Leverage a deep understanding of language, tone, and user intent to ensure agent interactions are intuitive, highly responsive, and creatively enabling within a media-generation environment.
Who You Are
- 2-3 Years of Experience: Professional experience in a technology-driven environment, with a strong preference for backgrounds that blend analytical and creative problem-solving with deep linguistic or communicative skills.
- Proven AI/Agentic Experience: Demonstrated hands-on experience building AI or agentic workflows within the past two years. Candidates without direct industry experience at an AI company must provide a robust portfolio of personal projects or products demonstrating these capabilities.
- Technical Literacy: Ability to manage a local development environment, navigate code repositories, write functional Python scripts, and utilize Git/GitHub. (Shipping production-level product code is not required; submitting PR’s, navigating codebase, and resolving errors is).
- Exceptional Command of Language: Advanced proficiency in written communication, with an intuitive understanding of how precise linguistic adjustments impact AI model outputs. In this domain, prose functions as code.
- Analytical Persistence & Qualitative Judgment: The persistence to iterate continuously through complex edge cases and the qualitative judgment to discern when a creative tool delivers an optimal user experience.
- AI Tool Proficiency: Advanced proficiency with AI coding assistants (e.g., Cursor, GitHub Copilot) and related AI productivity tools.
Bonus Points
- Experience building and operating Reinforcement Learning (RL) pipelines.
- Diverse hybrid backgrounds (e.g., intersections of humanities-focused study/role and Computer Science, Linguistics, or Data Analysis, or deeply technical self-taught endeavors).
- Experience with AI media generation, editing, and manipulation across modalities (video, images, 3D).
- Deep familiarity with creative workflows and the foundational tools digital artists use to craft visual narratives.