ML Engineer for AI-native Risk Intelligence

ByteDance; San Jose (CA); On-site; USD 162,000 - 388,000; Full time

Benefits offered by this job

Medical insurance

401(k) with company match

Parental leave

Disability & life insurance

RSUs

Job summary

ByteDance seeks a PhD-level researcher to develop and implement AI-native risk intelligence systems for its products and platforms in San Jose. You will prototype and validate new ML approaches, collaborating with Product and Tech teams to strengthen automation and risk defense infrastructure.

The role emphasizes building scalable data querying capabilities, defining risk metrics, and communicating insights through clear documentation and presentations. Onboarding by year-end is expected.

Qualifications

• Candidates completing or recently completed a PhD in CS/CE or related field.
• Strong foundation in ML theories and applications including ensemble methods and deep learning.
• Experience with Python, SQL/Hive, and Hadoop; solid data structures/algorithms knowledge.

Responsibilities

• Develop and implement ML algorithms to manage business risks in TikTok products.
• Prototype novel solutions, run experiments, and provide insights to product/tech teams.
• Collaborate with multidisciplinary teams to enhance automation processes.
• Build data querying infrastructure for real-time and offline analysis.
• Define risk metrics and align teams to numeric risk targets.
• Document processes and communicate results via writing, visuals, and presentations.

Skills

Ensemble methods

Deep neural networks

Reinforcement learning

Graph theory

Unsupervised learning

Python

SQL/Hive

Hadoop

Data structures & algorithms

Problem solving

Education

PhD in Computer Science or related

Tools

Python

SQL/Hive

Hadoop

Job description

ByteDance seeks a PhD-level researcher to develop and implement AI-native risk intelligence systems for its products and platforms in San Jose. You will prototype and validate new ML approaches, collaborating with Product and Tech teams to strengthen automation and risk defense infrastructure.

The role emphasizes building scalable data querying capabilities, defining risk metrics, and communicating insights through clear documentation and presentations. Onboarding by year-end is expected.