Machine Learning Engineer Intern (Risk & Integrity) - 2027 Summer

Role overview

Responsibilities

• Work with large-scale data, logs, metrics, or experiments to understand user search behavior and improve product or system quality.
• Collaborate with engineering, product, data science, and machine-learning partners to define requirements and deliver project milestones.
• Write clear, maintainable code and documentation for assigned workstreams.
• Communicate progress, risks, and learnings with mentors and stakeholders throughout the internship.

Minimum qualifications

• Currently pursue a bachelor's degree in computer science, computer engineering, software engineering, or a related technical field.
• Commit to a 12-week internship during Summer 2027.
• Have a solid machine-learning foundation, including Transformer and GNN architectures for large-scale integrity challenges with social graph and behavior sequence data.
• Have experience designing model evaluation systems that balance technical performance with product goals.
• Demonstrate strong engineering and coding skills in at least one of Python, C++, or Go.
• Be familiar with distributed computing such as Spark or Flink and deep-learning frameworks such as PyTorch.

Preferred qualifications

• Pursuing a master's degree in a related technical discipline is preferred.
• Experience building LLM-based agents for task planning, tool use, or closed-loop autonomous decision-making is preferred.
• Experience with LLM post-training or fine-tuning, including SFT, RLVR, or RLHF, for risk domains is preferred.
• Experience in video understanding or multimodal representation integrating visual, audio, and textual features is preferred.

Workplace

• The company is shifting to a fully in-person schedule of up to five days per week.

Compensation

• The hourly rate for this position in the selected city is USD 45 per hour.

Intern benefits

• Interns have day-one access to health insurance, life insurance, wellbeing benefits, paid holidays, and paid sick time.
• Interns who are not working 100% remotely may be eligible for a housing allowance.