Tech Lead AI Software Engineer - Creative AI Agents (TikTok)
TikTok
- Location
- San Jose, California, United States of America
- Employment
- Full Time
- Work model
- On-Site
- Level
- Senior
- H-1B history
- 148 approvals (FY2023)
Skills
About this role
The AIGE (AI-Generated Effects) team is building AI-native creative tools that enable TikTok creators to turn natural-language and multimodal ideas into high-quality, interactive effects. By combining agentic AI, multimodal foundation models, generative 2D/3D technologies, and professional effect-authoring tools, we aim to make advanced effect creation simple and accessible to everyone—significantly narrowing the gap between an idea and a polished, playable creative experience.
We are looking for a Tech Lead AI Software Engineer to join the TikTok AIGE team! Working at the intersection of Generative AI and Graphics, you will set the technical direction and own the end-to-end delivery of the next-generation AI Effect Generation system. You will partner with product, design, engineering, and AI/ML teams to turn ambiguous creator and platform needs into reliable technologies and intuitive experiences, from early research and prototyping through launch and long-term evolution, empowering millions of TikTok creators.
Responsibilities - Define the technical vision and roadmap for TikTok's AR Effect Generation AI system, identifying high-impact research opportunities and land in impactful TikTok product features. - Own the end-to-end architecture and development of key AI Effect Generation system, from problem discovery and technical design to implementation, rollout, and long-term maintenance; across data collection, context engineering, evaluation, and product integration. - Lead the research and productization of generative and agentic AI techniques for visual, interactive, and 3D content creation, integrating multimodal models with graphics engines and creator workflows while balancing quality, controllability, latency, reliability, safety, and delivery speed. - Establish robust experimentation, evaluation, and observability practices—including offline metrics, staged rollouts, A/B experiments, tracing, and quality monitoring, use online results to guide future research and product decisions. - Collaborate with product, design, engineering, and AI/ML partners to translate ambiguous creator and platform needs into clear technical strategies, align stakeholders on trade-offs, and deliver measurable user impact. - Provide technical leadership and mentorship to engineers and researchers, identify reusable abstractions across projects, raise engineering and research standards, and foster a culture of ownership, collaboration, and innovation.