Research Engineer – Ads Core ML & Ranking

TikTok; San Jose (CA); Sur place; USD 162 000 - 317 000; Plein temps

Avantages offerts par ce poste

Health insurance

401(k) with company match

Parental leave

Disability insurance

Life insurance

Wellbeing benefits

Paid holidays

Paid sick days

Paid personal time

Résumé du poste

TikTok is hiring for an ML/RL/LLM research engineer for the Ads Core ML Ranking team. You will contribute to automatic delivery products and revenue growth by applying state-of-the-art models and frameworks. The role emphasizes cross-team collaboration and global impact.

Ideal candidates have strong ML/DL background, experience with large-scale systems, and a passion for exploring frontier techniques like LLM-based ranking and resource allocation optimization.

Qualifications

• BS/MS in Computer Science, Statistics, OR related field.
• Experience in ML, DL, statistical models.
• Proficient in C/C++ and Python.
• Familiar with ML frameworks (TensorFlow/PyTorch/MXNet).
• Familiar with online experimentation and big data systems (Hadoop, Spark).
• Curiosity towards learning new technologies.

Responsabilités

• Optimize efficiency across the advertising funnel (Recall/Rough-sort, Fine-sort, CTR/CVR, format/creative personalization).
• Research and develop a global advanced advertising delivery system using ML/DL, RL, LLM, and scaling laws in ads.
• Design and set up system frameworks and standards to improve efficiency for various business needs.
• Collaborate with product and business teams across scenarios with global impact.

Connaissances

C/C++

Python

ML frameworks

Online experimentation

Formation

BS/MS in CS or related field

Outils

TensorFlow

PyTorch

MXNet

Hadoop

Spark

Description du poste

TikTok is hiring for an ML/RL/LLM research engineer for the Ads Core ML Ranking team. You will contribute to automatic delivery products and revenue growth by applying state-of-the-art models and frameworks. The role emphasizes cross-team collaboration and global impact.

Ideal candidates have strong ML/DL background, experience with large-scale systems, and a passion for exploring frontier techniques like LLM-based ranking and resource allocation optimization.