Research Scientist / Engineer – Foundation Model: Core Research

• 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.

• A Bachelor's, Master's, or PhD degree in Computer Science, Machine Learning, Physics, or Mathematics is essential.
• A 'first-principles' intuition for scaling. You don't just follow the literature; you understand why certain architectures succeed or fail at scale.
• Fluent in the language of frontier AI. You see research and engineering as a single, unified discipline.
• 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.

• 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.

Compensation

About Luma