Applied Research Scientist/Engineer

Luma AI

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.