Luma AI

    Staff Product Engineer | Generative Ads

    Luma AI
    Posted 12/6/2025Mid Level
    Full-time
    Technology
    Applied AI
    Research Collaboration
    Ads Intuition
    Vision-Language Models
    Generative AI

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    Job Description

    The Opportunity Luma AI is building the next era of AI with Omni models that can see, hear, and understand the world. As a full-stack company, we integrate research, engineering, and product into a single unified loop. This unique position allows us to deploy resources that few other companies can match to solve the hardest problems in multimodal intelligence.

    Where You Come In You will join the core engineering group to productionize novel research capabilities for a new high-impact product. This role requires an engineer who can translate the complexity of large vision-language models (VLMs) into magical user experiences. You will work directly with our research team to rapid-prototype and optimize inference pipelines that power automated creative workflows.

    What You Will Build Research-to-Product Pipelines: Build the systems that take frontier research concepts and turn them into reliable product capabilities, such as automated video generation and editing. Generative Feedback Loops: Design systems where real-time performance data informs the generation process, creating a continuous loop of creative improvement. Applied ML Strategy: Lead the technical definition of how we apply GenAI to the advertising domain, navigating trade-offs between model quality, latency, and control.

    The Profile We Are Looking For Applied AI Expert: You have hands-on experience with LLMs or GenAI tools and know how to leverage them to solve tangible product problems. Research Collaboration: You are comfortable operating in a fast-paced environment where engineering influences research, and you want to be in the room where core decisions are made. Ads Intuition: You understand the core metrics that drive value for advertisers and can translate creative quality into technical evaluation metrics.

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