PhD ML Engineer: Recommender Systems at Scale & Low Latency

TikTok; San Jose (CA); On-site; USD 220,000 - 320,000; Full time

Job summary

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.

Responsibilities

• 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.

Skills

Recommender systems

Distributed training

CUDA/CUDA kernels

Low-latency systems

Generative retrieval

Large-scale ML

Education

PhD in CS/ML/EE

Tools

CUDA

Triton

Distributed frameworks

Python/C++

Job description

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.