Senior Research Scientist

About the role

The Mail Intelligence team is responsible for building the next generation of platforms and services that enable Yahoo to deliver deeply personalized, intelligent, and context-aware experiences to hundreds of millions of users globally. We process billions of messages and manage data on a petabyte scale. Using cutting-edge algorithms, we extract knowledge and interconnect information from diverse sources to simplify our users' lives.

Responsibilities

• Lead R&D: Drive the research and development of deep learning models specifically tailored for large-scale email and communication data.
• Model Optimization: Fine-tune and adapt open-source foundation models using parameter-efficient techniques (LoRA, adapters) and quantization-aware training.
• Efficiency at Scale: Design and implement knowledge distillation to transfer complex capabilities into smaller, high-performance models.
• Modern Evaluation: Develop robust evaluation frameworks, including LLM-as-a-judge methodologies and human-in-the-loop validation.
• Product Integration: Build repeatable, scalable training workflows for high-throughput production environments.
• Future-Proofing: Explore agent-based systems, tool-use paradigms, and long-term generative AI roadmaps.
• Mentorship: Raise the bar for technical excellence by guiding junior researchers and contributing to the broader team.

Qualifications

• PhD (preferred) or Master’s degree in Computer Science, Machine Learning, NLP, or a related field.
• 4+ years of hands-on experience in applied machine learning and deep learning, with significant hands-on work in NLP and generative models at scale.
• Demonstrated experience fine-tuning LLMs using LoRA or other parameter-efficient methods.
• Experience with knowledge distillation, model compression, and/or training smaller models from larger teacher models.
• Deep understanding of transformer architectures, including encoder-only models, decoder-only models, and encoder-decoder models, as well as modern generative transformer techniques.
• Experience designing evaluation frameworks for generative systems, including prompt-based evaluation and LLM-as-a-judge approaches.
• Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow, along with Hugging Face tooling.
• Experience building scalable data pipelines and training workflows for large datasets.
• Strong experimental rigor and ability to translate research ideas into production-ready systems.
• Excellent communication skills and ability to operate effectively in cross-functional, fast-moving environments.
• GCP experience preferred.

Preferred Qualifications

• Experience deploying and optimizing models for on-device or resource-constrained environments (quantization, pruning, distillation).
• Experience with agent frameworks, tool use, or multi-step reasoning systems.
• Familiarity with reinforcement learning, preference optimization, or alignment techniques.
• Experience with large-scale distributed training and inference.
• Publications, patents, or open-source contributions in NLP or generative AI.
• Experience working with cloud platforms (GCP, AWS) and large-scale experimentation infrastructure.

Pay

$143,625.00 - $299,375.00/yr

Schedule

Flexible hybrid work options.