ML Engineer III - Recs & Ads Personalization

Socket.dev; Mountain View (CA); On-site; USD 147,000 - 210,000; Full time

Job summary

Socket.dev is seeking a software engineer to build end-to-end machine learning systems on large-scale data and contribute to production-grade recommendation models. You will work on model deployment, evaluation, optimization, and data processing to improve ranking, personalization, and search quality.

The role involves applying state-of-the-art ML techniques and tools such as TensorFlow and TPU, with a focus on scalable, reliable infrastructure.

Qualifications

• Bachelor’s degree or equivalent practical experience.
• 2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree.
• 1 year of experience with end-to-end machine learning (e.g., model deployment, model evaluation, optimization, data processing, debugging).
• 1 year of experience building and deploying recommendation systems models (retrieval, prediction, ranking, personalization, search quality, embedding) in production.

Responsibilities

• Write product or system development code.
• Build end to end machine learning systems on large scale data.
• Research novel ideas on model architectures and analysis, and then build, experiment and deploy them to improve prediction qualities.
• Work with latest ML techniques, tools like TensorFlow, Adbrain, TPU, and Recommendation systems.

Skills

Software development

End-to-end ML

Production ML systems

Recommendation systems

Education

Bachelor’s degree or equivalent practical experience

Master’s degree or PhD preferred

Tools

TensorFlow

TPU

Adbrain

Job description

Socket.dev is seeking a software engineer to build end-to-end machine learning systems on large-scale data and contribute to production-grade recommendation models. You will work on model deployment, evaluation, optimization, and data processing to improve ranking, personalization, and search quality.

The role involves applying state-of-the-art ML techniques and tools such as TensorFlow and TPU, with a focus on scalable, reliable infrastructure.