Remote ML Engineer: Personalization & Recommender Systems

Pinterest, Inc.; United States; Hybrid; USD 120,000 - 180,000; Full time

Job summary

Pinterest, Inc. is seeking a full-time Machine Learning Engineer to advance personalization across product surfaces. You will collaborate with cross-functional teams and contribute to ML model improvements while supporting remote work or working from the San Francisco, Palo Alto, or Seattle offices.

The role focuses on building data pipelines, experimenting with ML and DL models, and driving rapid experimentation and product launches across Homefeed, Ads, Growth, Shopping, and Search.

Qualifications

• 2+ years of industry experience with machine learning methods such as user modeling and recommender systems.
• Hands-on experience in building data processing pipelines and large-scale machine learning systems.
• Bachelor's degree in computer science, machine learning, statistics, or a related field.
• Familiarity with big data technologies like Hadoop or Spark.
• Experience with AI coding assistants and LLM-powered productivity tools is a plus

Responsibilities

• Develop and implement advanced machine learning and deep learning technologies to improve personalization on the platform
• Experiment with and refine ML models for diverse product areas including Homefeed, Ads, Growth, Shopping, and Search
• Utilize data-driven approaches to enhance candidate retrieval and support rapid experimentation and product launches

Skills

Machine learning

User modeling

Recommender systems

Data pipelines

Large-scale ML systems

Big data

Education

Bachelor's degree in computer science, machine learning, statistics, or a related field

Tools

Hadoop

Spark

Job description

Pinterest, Inc. is seeking a full-time Machine Learning Engineer to advance personalization across product surfaces. You will collaborate with cross-functional teams and contribute to ML model improvements while supporting remote work or working from the San Francisco, Palo Alto, or Seattle offices.

The role focuses on building data pipelines, experimenting with ML and DL models, and driving rapid experimentation and product launches across Homefeed, Ads, Growth, Shopping, and Search.