Machine Learning Engineer Intern (Infrastructure Platform) - 2026 Start (PhD)

for an internship in 2026. PhD Internships at TikTok aim to provide students with the opportunity to actively contribute to our products and research, and to the organization's future plans and emerging technologies.

PhD internships at TikTok provide students with the opportunity to actively contribute to our products and research, and to the organization's future plans and emerging technologies. Our dynamic internship experience blends hands-on learning, enriching community-building and development events, and collaboration with industry experts.
Applications will be reviewed on a rolling basis - we encourage you to apply early. Please state your availability clearly in your resume (Start date, End date).

Fall Start Dates:

• August 10th, 2026
• August 24th, 2026
• September 8th, 2026
• September 21st, 2026

Candidates who pass resume screening will be invited to participate in TikTok's technical online assessment.

Responsibilities:
This internship provides students the opportunity to join one of our engineering teams where you will have the opportunity to:

• Apply technical expertise in large-scale data analysis, experimentation, predictive modeling, and performance analytics to support business and infrastructure capacity planning, improve service performance and stability, optimize cost and utilization of data centers, cloud services and networks.
• Assist cross-functional teams in prioritizing infrastructure development, optimizing server performance, and setting measurable objectives, diagnose performance bottlenecks, and drive continuous improvements to enhance TikTok's infrastructure stability and growth.

Qualifications

Minimum Qualifications:

• Must be able to commit to a 12-week full-time work period during Fall 2026
• Currently pursuing a PhD degree in Artificial Intelligence, Computer Science, Operations Research, Automation, Statistics, Mathematics, or a related technical discipline
  -Strong proficiency in machine learning and statistics, including convex optimization, time series forecasting, regression, classification, clustering, anomaly detection, etc.
• Proficiency in Python with excellent coding skills; familiarity with data processing libraries (Pandas, NumPy); and experience with at least one ML/deep learning framework (TensorFlow, PyTorch, or Scikit-learn)
• Basic proficiency in SQL for querying structured datasets.

Preferred Qualifications

• Strong teamwork and communication skills
• Published papers in ML/AI conferences (NeurIPS, ICML, AAAI) or data center-focused conferences (IEEE Data Center Dynamics) or experience in ML competitions (Kaggle) is preferred
• Knowledge of data center operations (e.g., server maintenance, cooling systems), cloud infrastructure (AWS/Azure/GCP), monitoring tools (Prometheus, Grafana), or network engineering is a plus
• Experience building or supporting AI bots/LLM-powered tools for technical operations is a plus
• Familiarity with machine learning/statistics fundamentals, distributed computing frameworks (e.g., Apache Spark), and big data engineering practices (e.g., data pipeline building) is a plus