ML Engineer II — Personalization at Scale

Yahoo Holdings Inc.; Richardson (TX); Hybrid; USD 111,000 - 231,000; Full time

Benefits offered by this job

Flexible hybrid work options

Job summary

Yahoo Mail is seeking an AI/ML engineer to design, build, and deploy high-throughput models powering personalized experiences at scale. You will work across global teams to integrate Mail Intelligence features into core platforms, continually improving performance and reliability.

You will leverage modern ML stacks and cloud-native practices to deploy models into production, collaborate with engineers, and maintain rigorous coding standards in a fast‑moving, inclusive environment.

Qualifications

• Bachelor's degree in CS, DS or related field or equivalent experience.
• 2+ years building, training, deploying ML models or data-heavy backend systems.
• Strong Python or Java; C++ a plus.
• Experience with PyTorch, TensorFlow, Hugging Face, or Scikit-learn.

Responsibilities

• Drive personalization at scale with high-throughput AI/ML and NLP models.
• Embed AI-driven workflows to optimize daily coding, testing, and iteration cycles.
• Process massive datasets using ML and big data pipelines.
• Build resilient systems with robust feedback loops and fallbacks.
• Architectural evolution: migrate backend ML services to public cloud (GCP).
• Collaborate with global engineering groups to integrate models into core platforms.
• Uphold engineering standards with code quality and reviews.

Skills

AI-First Mindset

Engineered for Scale

Curious & Adaptable

Collaborative Partner

Pragmatic Problem Solver

Python

Java

C++

Education

Bachelor's degree in Computer Science, Data Science, or related technical field

Tools

PyTorch

TensorFlow

Hugging Face

Scikit-learn

vLLM

TensorRT-LLM

ONNX Runtime

Spark

Kafka

Job description

Yahoo Mail is seeking an AI/ML engineer to design, build, and deploy high-throughput models powering personalized experiences at scale. You will work across global teams to integrate Mail Intelligence features into core platforms, continually improving performance and reliability.

You will leverage modern ML stacks and cloud-native practices to deploy models into production, collaborate with engineers, and maintain rigorous coding standards in a fast‑moving, inclusive environment.