Research Scientist - Large Language Model
Luma AI
Software Engineering, Data Science
Palo Alto, CA, USA
Posted on Mar 14, 2026
About Luma AI
Luma’s mission is to build AGI. We believe that intelligence emerges from large-scale foundation models that can reason, plan, and communicate with depth and precision. Language models are central to this vision — serving as the backbone for reasoning, world modeling, and interaction.
To advance this mission, we build and operate the full stack end-to-end, spanning foundation models, large-scale training infrastructure, inference systems, and real-world products. This tight integration allows us to push research forward while shipping impactful systems at scale. Backed by a recent $900M Series C and our partnership with Humain to build a 2 GW compute supercluster (Project Halo), we are scaling the next generation of frontier language models.
Where You Come In
This is a rare opportunity to help define the future of large-scale language models. You will work across the entire lifecycle of model development — from large-scale pre-training, to targeted mid-training, to post-training alignment and capability refinement.
You will operate at the frontier of scaling laws, reasoning, and alignment, directly shaping how foundation models learn, generalize, and behave in real-world deployments.
What You’ll Do
This role spans both the “science” and “engineering” dimensions of research — two aspects that are equally important.
You will work across modeling, data, systems, and evaluation.
Modeling
Coding and structured generation
Knowledge grounding and factuality
Planning and agentic behavior
Instruction following and alignment
Luma’s mission is to build AGI. We believe that intelligence emerges from large-scale foundation models that can reason, plan, and communicate with depth and precision. Language models are central to this vision — serving as the backbone for reasoning, world modeling, and interaction.
To advance this mission, we build and operate the full stack end-to-end, spanning foundation models, large-scale training infrastructure, inference systems, and real-world products. This tight integration allows us to push research forward while shipping impactful systems at scale. Backed by a recent $900M Series C and our partnership with Humain to build a 2 GW compute supercluster (Project Halo), we are scaling the next generation of frontier language models.
Where You Come In
This is a rare opportunity to help define the future of large-scale language models. You will work across the entire lifecycle of model development — from large-scale pre-training, to targeted mid-training, to post-training alignment and capability refinement.
You will operate at the frontier of scaling laws, reasoning, and alignment, directly shaping how foundation models learn, generalize, and behave in real-world deployments.
What You’ll Do
This role spans both the “science” and “engineering” dimensions of research — two aspects that are equally important.
You will work across modeling, data, systems, and evaluation.
Modeling
- Architect and scale large autoregressive language models.
- Design improved pre-training objectives to enhance reasoning, knowledge retention, and compositional generalization.
- Develop mid-training strategies such as continued pre-training, domain adaptation, curriculum learning, and synthetic data integration.
- Advance post-training techniques, including instruction tuning, preference optimization, reinforcement learning, distillation, and inference-time compute scaling.
- Study and improve long-context modeling, planning depth, and multi-step reasoning behavior.
- Curate and construct massive, high-quality text corpora for pre-training.
- Design synthetic data pipelines for reasoning, tool use, mathematics, coding, and structured problem solving.
- Develop filtering, mixture weighting, and curriculum strategies that shape emergent capabilities.
- Formulate new tasks that improve coherence, logical consistency, factual grounding, and robustness.
- Train frontier-scale language models across large GPU clusters.
- Optimize distributed training (data, tensor, pipeline parallelism), mixed precision, and memory efficiency.
- Build infrastructure for large-scale experimentation, ablations, and reproducibility.
- Improve inference efficiency and support scalable deployment.
- Define and build evaluation frameworks for language intelligence, including:
Coding and structured generation
Knowledge grounding and factuality
Planning and agentic behavior
Instruction following and alignment
- Track capability development across pre-training, mid-training, and post-training.
- Close the loop between evaluation signals and data/model improvements.
- Strong foundation in machine learning and large language models.
- Deep understanding of autoregressive transformers and large-scale training dynamics.
- Experience with pre-training large models and/or post-training techniques such as instruction tuning, RLHF, preference optimization, or distillation.
- Hands-on experience with PyTorch and distributed training at scale.
- Comfortable operating across research and production environments.
- Experience training frontier-scale language models from scratch.
- Research contributions in scaling laws, reasoning, alignment, or inference-time compute.
- Experience designing large-scale synthetic reasoning data.
- Expertise in long-context modeling or structured reasoning systems.
- Experience optimizing models for real-world deployment constraints.