Machine Learning Engineer - Live Content AI (Multimodal)

TIKTOK PTE. LTD.; Singapore; On-site; SGD 80,000 - 120,000; Full time

Job summary

A leading tech company in Singapore is seeking a professional to join their Live Streaming Recommendation Core Technology Team. This role involves optimizing algorithms related to live streaming recommendations, leading the development of multimodal large models, and ensuring effective content moderation processes. The ideal candidate will possess a Master's degree, along with strong skills in multimodal learning and computer vision. Experience at product-oriented IT companies is highly desirable. Join us to drive innovation and technical excellence.

Qualifications

• Master technologies related to multimodal understanding.
• Strong sense of ownership and communication skills.
• Creative and independent thinker with problem-solving skills.
• Leadership experience in key projects within relevant fields.
• Experience in end-to-end deployment of content understanding technologies is a plus.

Responsibilities

• Optimize algorithms for live streaming content.
• Lead training and optimization of multimodal models.
• Build automated review architecture for content moderation.
• Iterate on understanding of live content representation.
• Explore recommendation technologies for live streaming.

Skills

Multimodal Learning

Computer Vision

Natural Language Processing

Creative problem-solving

Excellent communication skills

Education

Master's degree or equivalent

Job description

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.

Diversity & Inclusion

TikTok 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 TikTok, our mission is to inspire creativity and bring joy. 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.

Responsibilities

The Live Streaming Recommendation Core Technology Team is primarily responsible for foundational models and distribution pipelines in live streaming recommendations, including model iteration for modules such as precision ranking, first stage ranking, and recall. The team's key focus is optimizing algorithmic efficiency by addressing business challenges unique to live streaming content, deeply understanding user needs, and building industry-leading recommendation models. Additionally, the team serves as a centralized platform to support iterative improvements across various verticals within live streaming, accelerating business growth.

• Responsible for algorithmic work in Multimodal Learning, Computer Vision (CV), and Natural Language Processing (NLP) for TikTok's global live streaming business. Explore and implement content understanding technologies across various business lines to drive metric growth and technical innovation.
• Lead the training and optimization of multimodal large models specifically for live streaming visual styles. Leverage capabilities in high‑quality data curation and experience in SFT & RL to achieve SOTA performance in business scenarios.
• Responsible for building and optimizing the fully automated review architecture for content moderation scenarios. Explore innovative applications of large models within the safety and moderation business.
• Continuously iterate on the understanding and representation capabilities of live content at various granularities. Explore multimodal recommendation technologies and promote their technical deployment in live streaming recommendation scenarios.
• Build robust content understanding technical capabilities, explore efficient few‑shot recognition and model distillation techniques to support diverse business needs in live streaming (e.g., content diversity recognition, interaction understanding, professionalism modelling).

Qualifications

Minimum Qualifications

• Master technologies and application experience related to multimodal understanding. Possess a deep understanding of the architecture and training frameworks of mainstream large models, and continuously track the latest advancements in related fields.
• Passionate about business with a strong sense of ownership and proactivity. Possess excellent communication skills and a strong team spirit.
• Creative and independent thinker with outstanding skills in experimental analysis and problem‑solving. Capable of proposing innovative ideas and validating them through practical implementation.
• Candidates who have led key projects or published papers in top‑tier conferences/journals in the fields of Multimodal Learning or Large Models are preferred.
• Experience in the end‑to‑end deployment of content understanding technologies in Recommendation, Search, or Moderation scenarios is a plus.

Preferred Qualifications

At least 2 yrs experience at large scale product oriented IT companies.