Principal Research Engineer

What you'll do

• Lead model training programs including pretraining, continued pretraining, supervised fine-tuning, and post-training.
• Improve model quality by modifying architectures, training data, learning objectives, optimizers, and hyperparameters.
• Design and implement controlled experiments, ablation studies, and evaluation methods for accuracy, reasoning, and safety.
• Diagnose and resolve technical issues like training instability, convergence failures, numerical problems, and data-quality defects.
• Develop reusable training, evaluation, and experimentation practices to support multiple research initiatives.
• Partner with infrastructure specialists to scale successful approaches across GPU clusters and improve training efficiency.
• Provide technical direction and mentorship while translating ambiguous goals into measurable milestones.

What we're looking for

• Bachelor's Degree in Computer Science, Machine Learning, Applied Mathematics, or related field and 6+ years of technical engineering experience.
• Master's Degree or Doctorate in a relevant field and 8+ years of technical engineering experience (preferred).
• Bachelor's Degree in a related field and 12+ years of technical engineering experience (preferred).
• Proficiency in coding with languages such as Python, C, C++, C#, or Java.
• Experience training language or foundation models through pretraining, continued pretraining, or post-training techniques (preferred).
• Experience improving model quality via architecture changes, learning objectives, optimization methods, and data curation (preferred).
• Experience developing and debugging machine learning systems using PyTorch or JAX (preferred).
• Experience with distributed training, GPU performance, and large-scale experimentation (preferred).