Senior Research Scientist

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.

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.

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.

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.

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.

Experience deploying and optimizing models for on-device or resource-constrained environments (quantization, pruning, distillation).

with agent frameworks, tool use, or multi-step reasoning systems.

Familiarity with reinforcement learning, preference optimization, or alignment techniques.

with large-scale distributed training and inference.

Publications, patents, or open-source contributions in NLP or generative AI.

working with cloud platforms (GCP, AWS) and large-scale experimentation infrastructure.

The material job duties and responsibilities of this role include those listed above as well as adhering to Yahoo policies; exercising sound judgment; working effectively, safely and inclusively with others; exhibiting trustworthiness and meeting expectations; and safeguarding business operations and brand integrity.

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