About Us

Founded in 2012, ByteDance's mission is to inspire creativity and enrich life. With a suite of more than a dozen products, including TikTok, Lemon8, CapCut and Pico as well as platforms specific to the China market, including Toutiao, Douyin, and Xigua, ByteDance has made it easier and more fun for people to connect with, consume, and create content.

Why Join ByteDance

Inspiring creativity is at the core of ByteDance's mission. Our innovative products are built to help people authentically express themselves, discover and connect - and our global, diverse teams make that possible. Together, we create value for our communities, inspire creativity and enrich life - a mission we work towards every day.

As ByteDancers, we strive to do great things with great people. We lead with curiosity, humility, and a desire to make impact in a rapidly growing tech company. By constantly iterating and fostering an Always Day 1 mindset, we achieve meaningful breakthroughs for ourselves, our Company, and our users. When we create and grow together, the possibilities are limitless. Join us.

Diversity & Inclusion

ByteDance is committed to creating an inclusive space where employees are valued for their skills, experiences, and unique perspectives. Our platform connects people from across the globe and so does our workplace. At ByteDance, our mission is to inspire creativity and enrich life. To achieve that goal, we are committed to celebrating our diverse voices and to creating an environment that reflects the many communities we reach. We are passionate about this and hope you are too.

About the Team

The E-commerce Recommendation Foundation team is dedicated to building the next-generation recommendation intelligence. We aim to develop a unified Foundation Model that supports multi-business and multi-scenario recommendation systems, covering the full pipeline from retrieval and ranking to re-ranking, and driving a comprehensive upgrade in intelligence and generative capability.

We believe the future of recommendation systems goes beyond predicting click-through rates - it lies in understanding the relationship between people and content, and in generating new connections. The team is exploring an event-sequence-driven generative recommendation paradigm, deeply integrating large language models (LLMs), multimodal understanding, reinforcement learning, and system optimization to advance recommendation systems toward general-purpose intelligent agents.

We value original exploration and encourage both research thinking and engineering excellence. Every team member is empowered to propose hypotheses and validate ideas in an open environment - your code and papers may help define the next paradigm of recommendation systems. We seek individuals with a general intelligence mindset to join us in redefining the future of recommendation.

Responsibilities

1. Build and optimize cross-scenario shared Foundation Models to enable unified modeling and efficient inference.

Advance the event-sequence-driven generative recommendation paradigm, integrating multimodal understanding and generative capabilities.

2. Apply LLM technologies across retrieval, ranking, and re-ranking stages participate in model training, inference optimization, and system co-design.

3. Explore the integration of LLMs / VLMs with recommendation systems to develop adaptive and evolving intelligent recommenders.

4. Research end-to-end generative recommendation and system optimization methods that balance efficiency and user experience.

Minimum Qualifications

1. Solid theoretical foundation in machine learning, deep learning, or information retrieval.

2. Proficiency in Python and familiarity with mainstream deep learning frameworks (e.g., PyTorch).

3. Strong passion for intelligent recommendation systems and a self-driven research mindset.

Preferred Qualifications

1. Experience in large-scale recommendation system development or large-model training, with notable technical achievements in a sub-area.

2. Research experience or publications in LLMs, multimodal learning, reinforcement learning, or generative recommendation.

3. Familiarity with pre-training and post-training processes for large language models (LLMs) or Foundation Models.

Large Recommendation Model Algorithm Engineer- Global E-Commerce