Description
Research Scientist - Large Language Model
Location: SF Bay Area, CA, Remote, International, London, UK
Department: Research
Location Type: HYBRID
Employment Type: FULL_TIME
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.
Data
- 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.
Systems
- 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.
Evaluation: 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.
Who You Are
- 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.
What Sets You Apart (Bonus Points)
- 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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