Applied Research Scientist / Engineer

Luma AI Luma AI · AI Frontier · New York, NY +2 · Applied Research & Engineering

Luma AI is seeking an Applied Research Scientist/Engineer to refine, personalize, and build the final capabilities and control interface of their multimodal foundation models, focusing on video. The role involves adapting and deploying models to production, working across modeling, data, systems, and evaluation to improve expressiveness, controllability, and personalization for creative workflows. This position sits at the intersection of research, product, and partnerships, aiming to bridge the gap between state-of-the-art and production-ready AI.

What you'd actually do

  1. You will leverage a toolkit spanning SFT, RL, personalization, distillation, control adapters, and more, to develop and maintain model variants purpose-built for user environments and creative partners.
  2. Architect the data engine for rapid adaptation. You will leverage proprietary, vertical-specific datasets to create specialized finetunes and improve future training recipes, ensuring our models rely on data that reflects real-world use cases.
  3. You will define and drive end-user quality – setting success metrics, building user-aligned evaluations, and iterating on the model/data/evals loop to meet strict fidelity and reliability targets in specific enterprise verticals.
  4. Partner closely with Product, Research, and Design to translate creative intent and user feedback into model behavior, intuitive controls, and production-ready capabilities for users and partners.

Skills

Required

  • Python
  • deep learning engineering
  • PyTorch
  • fine-tuning
  • personalization
  • domain adaptation
  • data curation
  • targeted distillation
  • interpretability
  • human-feedback-driven refinement
  • visual generative models (diffusion/transformers or related architectures)

Nice to have

  • Contributions to state-of-the-art models in image/video generation
  • Experience collaborating with creative partners (VFX, animation, film, design tools)
  • Track record building workflows/tools that materially improve iteration speed and evaluation rigor
  • Familiarity with large-scale training infrastructure and distributed systems (Ray, Slurm, Kubernetes)

What the JD emphasized

  • final capabilities
  • control interface
  • video foundation models
  • expressive, controllable, and personalized
  • last mile challenges
  • modeling, data, systems, and evaluation
  • Controllability and Features
  • Personalization
  • End-User Quality
  • production-ready capabilities

Other signals

  • multimodal foundation models
  • video foundation models
  • personalization
  • controllability
  • end-user quality