The opportunity to work on TikTok’s audio and video creation experiences and build the next generation of AI-powered creative tools for users worldwide.
Under the guidance of a mentor, participate deeply in the development of core TikTok Search scenarios such as the search results page. Work closely with Product, Design, and Algorithm teams, and drive feature iterations through A/B testing and data analysis to continuously optimize the search experience for hundreds of millions of users.
Analyzing, mining, and modeling large volumes of business data to build a comprehensive algorithmic system that extracts valuable information regardless of rules or machine learning algorithms.
Lead, coach, and develop a high-performing machine learning and AI team, fostering a culture of ownership, technical excellence, and continuous learning.
Own the Applied AI intelligence layer of the Channel Sales AIML Platform, defining how AI reasoning and domain intelligence are applied across Store, Shopping, Learning, and Decision Intelligence.
Define and execute a 2–3 year HR Technology, AI and digital transformation strategy and roadmap, balancing long-term transformation with tangible quick wins.
Prompt Engineering & Evaluation: Design and execute sophisticated test cases using prompt injection and adversarial testing to evaluate the AI’s boundaries and responses.
AI Model Test, Evaluate, Validate, Verify: Design and conduct systematic tests to evaluate AI model safety, security, and performance. This includes researching and applying various security/safety test domains, industry benchmarks, and advanced evaluation methodologies (e.g., benchmark based evaluations, LLM-as-a-Judge, etc).
Contribute to analytical work across asset class and strategy teams including macro, equities, fixed income, alternatives to support the team's investment views.
Define the technical strategy for the agentic AI platform, covering orchestration, agent design patterns, memory management, model gateway, and developer tooling.
Monitor adherence to client restrictions and investment risk frameworks, including watchlist, concentration risk, liquidity risk, portfolio turnover to ensure portfolios remain aligned with defined mandate risk objectives.
Translate priority AI use cases into well-defined AI agents and agentic workflows, with clear business objectives, ownership, success measures and control requirements.