Published on
Feb 27, 2026
Machine Learning Engineer
Company: Yahoo
Employment Type: Full-Time
Location: 100% Remote – USA (occasional in-person events with notice)
Compensation: $111,000 – $231,250 (USD)
Category: Data, AI & Analytics
Close date or Apply by date: Not disclosed
WHY THIS ROLE MADE THE CUT
Fully remote (no hybrid or office requirements)
Full-Time role
Job is listed on company site with a direct link to apply
Legitimate, established company
Posted within the last 14 days OR includes a clear deadline to apply
Clear salary range is disclosed
ROLE SNAPSHOT
Yahoo is hiring a Machine Learning Engineer to work alongside experienced ML engineers, data scientists, and product teams to support development and deployment of intelligent systems powering recommendations, content understanding, and personalization features for hundreds of millions of users globally. You'll contribute to development and maintenance of ML systems focused on recommendation, ranking, and content enrichment, support integration of ML models into backend services, build and maintain data pipelines enabling training and deployment, monitor production model performance and debug issues, and contribute to MLOps practices ensuring model reliability and scalability. This role requires Bachelor's or Master's in Computer Science or related field, 3-5 years of software engineering experience preferably including ML systems, solid programming skills in Python or Java, exposure to ML frameworks (Scikit-learn, TensorFlow, PyTorch), and experience with distributed data processing systems (Apache Flink, Beam, Spark, Storm).
KEY WORDS TO INCLUDE IN YOUR RESUME/COVER LETTER IF YOU APPLY:
Machine learning engineering
Python or Java programming
ML frameworks (TensorFlow, PyTorch, Scikit-learn)
Distributed data processing (Spark, Flink, Beam, Storm)
MLOps and model deployment
Cloud platforms (AWS, GCP)
Recommendation and ranking systems
Data pipelines
CI/CD pipelines
NLP techniques
Keywords are suggested based on the language used in the employer's job description to help applicants align with automated screening systems.
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