Machine Learning Scientist (Multiple Positions)

About TikTok
TikTok is the leading destination for short-form mobile video. At TikTok, our mission is to inspire creativity and bring joy.
TikTok's global headquarters are in Los Angeles and Singapore, and we also have offices in New York City, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo.

Why Join Us
Inspiring creativity is at the core of TikTok's mission. Our innovative product is 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 bring joy - a mission we work towards every day.
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.
Every challenge is an opportunity to learn and innovate as one team. We're resilient and embrace challenges as they come.
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.

About the Team
Our team plays a crucial role in ensuring the company’s success. We seek people who are willing to learn and put in the effort to solve problems. Our challenges are not your regular day-to-day problems - you’ll be part of a team that’s developing new solutions to new challenges. It’s working fast, at scale, and we’re making a difference. We are looking for talents to join us on this exciting journey!

Responsibilities
Conduct cutting-edge research in retrieval and recommendation algorithms and multi-modality modeling with focus on developing machine learning (ML) models for understanding short-form videos using various modalities, including video frames, text descriptions, and background audio that are fundamental to moderation, searching, and recommendation for short-form videos.
Work on Data-driven training (with hyperparameters search) of the neural network models.
Perform offline evaluation (pseudo-A/B test) of the trained models using real-world short-form videos and document each model's detailed specification, training procedure, and evaluation result.
Communicate with the internal content moderation team on the technical needs (specifications and resources) to develop software for neural network models.
Review and analyze latest literature on the state-of-the-art neural network models for video understanding.
Publish research papers that benefit the academic community.