Research Scientist - Large Language Model Job at Luma AI (Palo Alto)

Job Description

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.

Job Responsibility

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: Multi-step reasoning and mathematical problem solving, 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

Requirements

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

Nice to have

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

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Research Scientist - Large Language Model

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