Applied Research Scientist/Engineer

You'll own the last mile of Luma's video foundation models: making them expressive, controllable, and personalized enough for the most demanding creative work. As an Applied Research Scientist / Engineer, you sit between research, product, and our creative partners, turning state-of-the-art models into something people actually ship with. This is a fullstack applied research role, so you'll move across modeling, data, systems, and evaluation rather than going deep in only one. The problems are specific and messy: a partner's fidelity target, an identity that has to hold across a scene, a control that has to behave. It suits someone who treats users as collaborators and cares more about real output quality than public benchmark numbers. If you'd rather optimize a single metric in isolation, this won't be a fit. What You'll Own Build and maintain model variants for specific user environments and creative partners, using SFT, RL, personalization, distillation, and control adapters. Architect the data engine for rapid adaptation, using proprietary vertical datasets to create specialized finetunes and sharpen future training recipes. Define and drive end-user quality: set the success metrics, build user-aligned evaluations, and run the model/data/eval loop to hit fidelity and reliability targets in enterprise verticals. Partner with Product, Research, and Design to turn creative intent and user feedback into real model behavior and production-ready controls. Close the gap between research prototypes and production systems so the work reaches users, not just papers. First 90 Days One way the first 90 could unfold. Days 1–30 — Immerse & Diagnose: Get deep on the current models and where they fall short on controllability and personalization for priority partners, and pick the first last-mile problem worth solving. Days 30–60 — Ship & Validate: Deliver a model variant or control that measurably improves output for a real creative workflow, backed by an evaluation that proves it. Days 60–90 — Scale & Systemize: Turn that into a repeatable adaptation and evaluation loop other verticals can reuse. What You Bring Strong ML fundamentals and deep experience with visual generative models (diffusion, transformers, or related architectures). Depth in at least one of: fine-tuning, personalization, domain adaptation, data curation, targeted distillation, interpretability, or human-feedback refinement. Hands-on Python and deep-learning engineering, ideally PyTorch, comfortable across prototypes and production. A product instinct: you treat end users and partners as collaborators and solve for their real problems. Nice to Have Contributions to state-of-the-art image or video generation models. Experience working with creative partners (VFX, animation, film, design tools). A track record building workflows or tools that speed up iteration and tighten evaluation. Familiarity with large-scale training infrastructure and distributed systems (Ray, Slurm, Kubernetes). About Luma: Luma's mission is to build unified general intelligence that can generate, understand, and operate in the physical world. We believe multimodality is critical for intelligence — the next step beyond language models comes from vision. Luma is an equal opportunity employer.