AI Risk & Multi-Agent ML Engineer (Real-Time Insights)

Bytedance; San Jose (CA); Hybrid; USD 150,000 - 230,000; Full time

Job summary

ByteDance in San Jose is seeking a highly qualified researcher to develop AI-native risk intelligence systems for its platforms. You will design, prototype, and validate advanced ML models to detect and mitigate risks in large-scale content and user interactions.

The role emphasizes collaboration with product and tech teams, data-driven risk metrics, and extensive experimentation. Candidates should have a PhD in CS and strong ML expertise, with a focus on reinforcement learning and graph-based

Qualifications

• PhD in Computer Science or related field is required or completing soon.
• Proficiency in modern ML theories and applications such as ensemble methods, deep nets, RL, and graph theory.
• Experience with Python, SQL/Hive, and Hadoop; plus strong algorithms/data-structures knowledge.

Responsibilities

• Develop and implement ML algorithms to manage business risks in ByteDance products and platforms.
• Prototype and validate novel risk solutions; provide insights to product and tech teams.
• Collaborate with cross-disciplinary teams to improve automation and risk defense.
• Build data querying infrastructure for real-time and offline analysis; improve risk metrics and accountability.
• Document processes and communicate results through writing, visuals, and presentations.

Skills

Ensemble methods

Deep learning

Reinforcement learning

Graph theory

Unsupervised learning

Problem solving

Data structures

Education

PhD in Computer Science

Tools

Python

SQL/Hive

Hadoop

Job description

ByteDance in San Jose is seeking a highly qualified researcher to develop AI-native risk intelligence systems for its platforms. You will design, prototype, and validate advanced ML models to detect and mitigate risks in large-scale content and user interactions.

The role emphasizes collaboration with product and tech teams, data-driven risk metrics, and extensive experimentation. Candidates should have a PhD in CS and strong ML expertise, with a focus on reinforcement learning and graph-based