ML Researcher – Foundational Models

About the role

As an ML Researcher focusing on foundational models, you will tackle open‑ended research questions that shape the next generation of large‑scale AI systems. This is not a fine‑tuning position; you will design and run large‑scale experiments to inform core architectural and training decisions.

Key responsibilities

• Drive research on model architecture, optimization, scaling behavior, training stability, and post‑training recipes for frontier‑scale foundational models.
• Design and execute ablation studies at meaningful scale, running end‑to‑end pre‑training experiments yourself.
• Translate research findings into concrete proposals for production training runs and own their implementation.
• Collaborate closely with infrastructure and data teams to address research questions at the system boundary.

Required profile

• PhD in Machine Learning, Computer Science, or a closely related field (or near completion).
• 3+ years of post‑PhD research experience with a strong publication record (first‑author papers at top‑tier venues such as NeurIPS, ICML, ICLR, ACL, EMNLP, or COLM).
• Hands‑on experience pre‑training transformer‑based language models from scratch (7B+ parameters), including full ownership of training runs.
• Demonstrated contributions to the open‑source LLM ecosystem (code, model releases, datasets, or major project contributions).
• Fluency in PyTorch and comfort with distributed training, plus strong intuition for experimental design.

Required skills

• PyTorch
• Distributed training
• Transformer‑based language model pre‑training (7B+ parameters)
• Large‑scale compute environments
• Experimental design and ablation methodology