Senior Applied ML Scientist for Large-Scale NLP

Yahoo

United States

Hybrid

USD 128,000 - 299,000

Full time

5 days ago
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Benefits offered by this job

Healthcare
401k
Education stipends
Backup childcare

Job summary

Yahoo Mail is seeking an Applied ML Researcher to bridge theory and production. You will advance transformer-based models, NLP workflows, and scalable evaluation for billions of emails, with a focus on low-latency inference and production tooling.

The role emphasizes practical impact, cross-functional collaboration, and mentorship within a fast-moving team. You will lead research on deep learning models, distillation, and tool use, while contributing to the development of scalable data pipelines

Qualifications

  • PhD (preferred) or MS in CS, ML, NLP or related field.
  • 6+ years of hands-on ML/DL experience at scale.
  • Experience fine-tuning LLMs with LoRA or similar methods.
  • Experience with knowledge distillation and model compression.

Responsibilities

  • Lead R&D for deep learning and generative AI models tailored for large-scale email data.
  • Define and advance fine-tuning/adaptation of open-source foundation models.
  • Design and implement knowledge distillation to run smaller high-performance models with low latency.
  • Establish robust evaluation frameworks and production-ready workflows.

Skills

Transformer architectures
NLP at scale
Python
PyTorch
Hugging Face tooling
Model compression

Education

PhD or MS in CS/ML/NLP

Tools

PyTorch
TensorFlow
Hugging Face tooling
GitHub Copilot
Cursor

Job description

Yahoo Mail is seeking an Applied ML Researcher to bridge theory and production. You will advance transformer-based models, NLP workflows, and scalable evaluation for billions of emails, with a focus on low-latency inference and production tooling.

The role emphasizes practical impact, cross-functional collaboration, and mentorship within a fast-moving team. You will lead research on deep learning models, distillation, and tool use, while contributing to the development of scalable data pipelines

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