Generative Recommender ML Engineer

TikTok; Seattle (WA); On-site; USD 154,000 - 301,000; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

401(k) with match

Parental leave

Disability insurance

Life insurance

Wellbeing benefits

Holidays

Sick days

Personal time

Job summary

TikTok is seeking a PhD-level researcher in Seattle to advance recommender systems through generative modeling, LLM-based understanding, and agentic architectures. You will explore generative retrieval, re-ranking, and efficient GPU training/inference to improve personalization for e-commerce contexts and live platforms.

The role emphasizes deep learning, RL, and scalable research across live systems, with opportunities to publish and contribute to cutting-edge AI for large-scale consumer

Qualifications

• PhD completed or near completion in CS/EE/Math/Stats or related field.
• Strong foundation in ML/DL with research or project experience in LLMs, RL, generative models, recommender systems, or IR.
• Proficient in Python and at least one mainstream DL framework (PyTorch/TF/JAX). Strong problem-solving and open-ended research skills.

Responsibilities

• Generative Recommendation Research: evolve recommender systems toward generative paradigms and optimize training/inference on GPUs.
• LLM for Recommendation Research: use LLMs, RL, and related techniques to enhance semantic understanding in e-commerce recommendations.
• Agentic Recommendation Research: build self-evolving agents for real-time personalized decisions and adjustments.
• Long-Term Value and User Experience Modeling: develop algorithms to measure and optimize LTV and user experience.

Skills

Deep learning

Python

PyTorch

Reinforcement Learning

Large Language Models

Education

PhD in Computer Science/EE/Math/Stats

Tools

PyTorch

TensorFlow

JAX

Job description

TikTok is seeking a PhD-level researcher in Seattle to advance recommender systems through generative modeling, LLM-based understanding, and agentic architectures. You will explore generative retrieval, re-ranking, and efficient GPU training/inference to improve personalization for e-commerce contexts and live platforms.

The role emphasizes deep learning, RL, and scalable research across live systems, with opportunities to publish and contribute to cutting-edge AI for large-scale consumer