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Luma AI Logo Luma AI · IT - Software Development

Research Scientist / Engineer – Foundation Model: Core Research

United States, Palo Alto 250000.00 - 450000.00 USD / Year · Job Posted March 13, 2026
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Job Description

This is a rare and foundational opportunity to define the future of multimodal AI. You will be at the forefront of architecting the intelligence governing our world-simulations—the reasoning core at the heart of our world-modeling efforts. This role offers the chance to bridge frontier research with magical, shipped products like Dream Machine and Ray3, solving novel problems where no playbook exists.

Job Responsibility

  • Unified Modeling & Efficiency Drive the core research that powers all of Luma's products — co-designing multimodal representations, advancing core algorithms for long-context training, and establishing rigorous scaling laws to predict performance across compute budgets
  • Alignment & Evaluation Close the gap between training loss and user experience. Develop proxy tasks and automated metrics that serve as the compass for research decisions — ensuring our models optimize for what actually matters to users, not just benchmarks
  • Research Infrastructure Build the engine for high-velocity research. Maintain production-research parity, ensure reproducibility, and design systems for rapid experimentation — so that novel ideas go from hypothesis to validated result as fast as possible

Requirements

  • A Bachelor's, Master's, or PhD degree in Computer Science, Machine Learning, Physics, or Mathematics is essential
  • A 'first-principles' intuition for scaling
  • Fluent in the language of frontier AI
  • Proven ability to design and rigorously analyze experiments and to articulate complex technical concepts effectively
  • Practical experience with distributed or high-performance computing environments, particularly managing and optimizing training runs on large-scale GPU clusters

Nice to have

  • A track record of publishing at top-tier venues (NeurIPS, ICML, ICLR) and a mission-driven, "first-principles" mindset
  • Infrastructure Expertise: Proven ability to build and lead research infrastructure for technical teams, ensuring production-research parity
  • Engineering Excellence: Strong commitment to software engineering best practices, including optimizing for code readability and reusability, implementing comprehensive unit and integration tests, and maintaining high documentation standards (necessary docstrings)
  • Experience with low-precision training and hardware-aware optimization for next-gen clusters
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