Senior Research Scientist

About this role

Yahoo Mail is the ultimate consumer inbox with hundreds of millions of users. It’s the best way to access your email and stay organized from a computer, phone or tablet. With its beautiful design and lightning fast speed, Yahoo Mail makes reading, organizing, and sending emails easier than ever.

A Little About Us The Mail Intelligence team is the brain behind the inbox. We are 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.

Building this knowledge provides many challenges in the areas of natural language processing, machine learning techniques, big data processing in order of petabytes. You will build tools and workflows to make it easier to manage and act on this vast information. You will apply your insights on the data to build innovative consumer applications for Yahoo. 

Whether it’s reinventing how people organize their day or building lightning-fast, beautiful mobile experiences, our team is transforming the way the world connects. Yahoo Mail is the ultimate consumer inbox.

A Lot About You You are a seasoned Applied ML Researcher who thrives at the intersection of theoretical innovation and production-grade execution. You don't just follow the latest LLM trends; you understand the mechanics of transformer architectures and how to optimize them for massive scale. You have expertise working across multiple ML and NLP spaces - including summarization, information extraction, classification, and ranking - at a very large scale. You have hands-on experience with knowledge distillation. You believe that a model is only as good as its evaluation framework and its ability to perform in a low-latency production environment. You combine strong research fundamentals with pragmatic large-scale production instincts and can independently drive ambiguous, high-impact initiatives. You thrive in a research-oriented environment and enjoy pushing the boundaries of innovation to deliver impactful solutions. You excel in transforming theoretical research into practical applications, especially in the domains of Natural Language Processing (NLP). You are a hands-on expert with experience training and evaluating large-scale models, including cutting-edge deep learning architectures. You are comfortable navigating ambiguity, driving high-impact initiatives independently, and mentoring others.

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.

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.

At Yahoo, we offer flexible hybrid work options that our employees love! While most roles don’t require regular office attendance, you may occasionally be asked to attend in-person events or team sessions. You’ll alway

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