Research Scientist / Engineer – Foundation Model: Core Research
Conducts core research on multimodal foundation models for world-simulations, bridging modeling, data, systems, and evaluation. Requires advanced degree in CS/ML, first-principles scaling intuition, and experience with large-scale GPU training.
About the job
What You'll Do
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.
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.
- Practical 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) 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
The base pay range for this role is $250,000 – $450,000 per year.
Skills
Multimodal Ai, Machine Learning, Gpu Clusters, Distributed Computing, Scaling Laws, Long-Context Training, Research Infrastructure, Low-Precision Training, PyTorch, JAX
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