Research Scientist - Large Language Model

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.

Your application are reviewed by real people.