Machine Learning Engineer Intern (Recommendations and Search) - 2027 Start (PhD) (Recommendations and Search)

Location: San Jose, United States; fully in-person schedule up to 5 days a week

Salary: $60 per hour

Company

hirify.global develops safeguards for U.S. user data, applications, algorithms, privacy, cybersecurity, trust, and safety.

What you will do

• Design, build, train, and evaluate machine learning models for recommendation systems and search ranking.
• Develop pipelines for feature extraction, model training, evaluation, deployment from offline to online environments, and A/B testing.
• Process and analyze large-scale user behavior data and catalog logs using distributed systems.
• Construct real-time and offline features for personalized, high-performing user experiences.
• Collaborate on machine learning products and research for TikTok U.S. users.

Requirements

• Currently pursuing a PhD in Computer Science, Computer Engineering, Software Engineering, or a related technical field.
• Availability for a 12-week internship during summer 2027; start and end dates should be included in the resume.
• Proficient coding skills and a strong foundation in algorithms and data structures.
• Effective communication and teamwork skills.

Nice to have

• Experience with recommendation systems, online advertising, information retrieval, natural language processing, machine learning, or large-scale data mining.
• Familiarity with scikit-learn, TensorFlow, or PyTorch.
• Critical, rational, result-oriented, and data-driven problem-solving approach.

Culture & Benefits

• Hands-on internship experience with opportunities to contribute to products, research, and emerging technologies.
• Community-building and professional development events with industry collaboration.
• Health insurance, life insurance, and wellbeing benefits from the first day.
• Ten paid holidays and paid sick time.
• Housing allowance may be available for interns who are not working 100% remotely.

Hiring process

• Applications are reviewed on a rolling basis.