Graduate ML Engineer - Build Scalable Recommender Systems

TikTok; San Jose (CA); Sur place; USD 220 000 - 320 000; Plein temps

Résumé du poste

TikTok is seeking a PhD-level researcher/engineer to join the Global E-Commerce Content Recommendation team in San Jose. You will build and rewrite an industrial recommender serving a billion‑scale user base across short-video, livestream, and product scenarios, delivering end-to-end retrieval, ranking, and blending with ultra-low latency.

The role emphasizes scaling models, generative retrieval, and injecting world knowledge, using CUDA/Triton kernels and distributed training.

Qualifications

• PhD in CS, ML, or related field.
• Experience with large-scale recommender systems.
• Strong publication record or industry impact.

Responsabilités

• Research and develop retrieval, ranking, and blending algorithms.
• Scale models to billions of users with ultra-low latency.
• Develop and optimize CUDA kernels and distributed training pipelines.

Connaissances

Recommender systems

Distributed training

CUDA/CUDA kernels

Low-latency systems

Generative retrieval

Large-scale ML

Formation

PhD in CS/ML/EE

Outils

CUDA

Triton

Distributed frameworks

Python/C++

Description du poste

TikTok is seeking a PhD‑level researcher/engineer to join the Global E‑Commerce Content Recommendation team in San Jose. You will build and rewrite an industrial recommender serving a billion‑scale user base across short-video, livestream, and product scenarios, delivering end-to-end retrieval, ranking, and blending with ultra-low latency.

The role emphasizes scaling models, generative retrieval, and injecting world knowledge, using CUDA/Triton kernels and distributed training.