Jobs · Engineering · California

Research Scientist / Engineer – Foundation Model: Core Research

Luma · San Francisco Bay Area · 1 wk ago
HybridEngineeringFull-time

What You'll Do

  • 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.
  • Closely align research with user experience by developing proxy tasks and automated metrics that serve as the compass for research decisions.
  • Build the engine for high-velocity research, maintaining production-research parity, ensuring reproducibility, and designing systems for rapid experimentation.

Who You Are

  • 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.
  • Past experience with distributed or high-performance computing environments, particularly managing and optimizing training runs on large-scale GPU clusters.

What Sets You Apart (Bonus Points)

  • A track record of publishing at top-tier venues (NeurIPS, ICML, ICLR).
  • 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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