Machine Learning Engineer Intern (AI)

TL;DR

Machine Learning Engineer Intern (AI): Designing and implementing machine learning algorithms for core product domains including recommendations, trust, and growth with an accent on building efficient end-to-end ML workflows. Focus on deploying production-grade ML models and solving complex data challenges at a global scale.

Location: Must be based in or able to commute to Palo Alto, California for hybrid work (3 days/week)

Salary: $47 per hour

Company

hirify.global is a global platform connecting millions of people through human relationships, behavioral science, and advanced machine learning technologies.

What you will do

• Design and implement machine learning algorithms for domains like recommendation, trust, profile, chat, and revenue.
• Collaborate with senior engineers to build and deploy ML solutions that advance core business goals.
• Gain practical experience formulating production-level ML problems.
• Participate in the annual hirify.globalship Hackathon and present projects to company leadership.
• Improve user experience through large-scale data and model optimizations.

Requirements

• Currently pursuing a BS/BA or MS in Computer Science or a related technical field.
• Proficient in Python programming.
• Foundational knowledge of computer science principles, data structures, and algorithms.
• Practical experience in designing and building machine learning models.
• Understanding of how to build efficient end-to-end ML workflows.
• Availability to work onsite in Palo Alto, California 3 days per week.

Nice to have

• Publications in top ML or data science conferences like NeurIPS, ICML, RecSys, or KDD.
• Experience deploying ML models in production environments.
• Familiarity with frameworks such as PyTorch, TensorFlow, or Keras.
• Experience with data tools like Databricks, Spark, or Airflow.
• Proficiency in Go, Java, or Scala.

Culture & Benefits

• Inclusive environment that celebrates diversity and unique perspectives.
• Mentorship opportunities with senior engineering staff.
• Engagement in a high-impact, global scale technology ecosystem.
• Opportunity to influence key product features through AI/ML research and deployment.