Key skills
AIArtificial IntelligenceLLMAgenticPerformance Optimization
About this role
We are looking for talented individuals to join our team in 2027. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Launch your career where inspiration is infinite at our Company.
Successful candidates must be able to commit to an onboarding date by end of year 2027. Please state your availability and graduation date clearly in your resume.
Team Introduction: Our team provides end-to-end security solutions for ByteDance’s large language model (LLM) business lines.
We identify and mitigate emerging risks across the entire lifecycle of model training, inference, and deployment, while also developing tools and systems to safeguard LLM assets and related applications.
By advancing the deployment of AI security technologies in real-world business scenarios, we have built an industry-leading defense framework to support the safe evolution of artificial intelligence technologies.
Topic Content: While agent-based applications are experiencing explosive growth, they have also introduced new security challenges. The expanded attack surface covering data, decision-making, execution, and supply chains, combined with their intricate interactions and complex infrastructure, has rendered traditional security technologies ineffective.
This topic aims to systematically research adversarial testing and evaluation technologies for foundation models and agents in the Agentic AI era, as well as a full-link defense system based on foundation model trusted privacy computing, to ensure the safe and stable development of the company's business related to foundation models, including:
1. Attack-defense detection and evaluation methods, testing benchmarks, and toolchains for multimodal foundation models and agents;
2. Risk defense technologies and trustworthy runtime security assurance for agent-based applications;
3. Training and inference technologies for foundation models and samples, ensuring data and model asset security;
4. Secure construction, performance optimization, and of confidential training and inference infrastructure for foundation models.
The base salary range for this position in the selected city is $212800 - $450000 annually.
Successful candidates must be able to commit to an onboarding date by end of year 2027. Please state your availability and graduation date clearly in your resume.
Team Introduction: Our team provides end-to-end security solutions for ByteDance’s large language model (LLM) business lines.
We identify and mitigate emerging risks across the entire lifecycle of model training, inference, and deployment, while also developing tools and systems to safeguard LLM assets and related applications.
By advancing the deployment of AI security technologies in real-world business scenarios, we have built an industry-leading defense framework to support the safe evolution of artificial intelligence technologies.
Topic Content: While agent-based applications are experiencing explosive growth, they have also introduced new security challenges. The expanded attack surface covering data, decision-making, execution, and supply chains, combined with their intricate interactions and complex infrastructure, has rendered traditional security technologies ineffective.
This topic aims to systematically research adversarial testing and evaluation technologies for foundation models and agents in the Agentic AI era, as well as a full-link defense system based on foundation model trusted privacy computing, to ensure the safe and stable development of the company's business related to foundation models, including:
1. Attack-defense detection and evaluation methods, testing benchmarks, and toolchains for multimodal foundation models and agents;
2. Risk defense technologies and trustworthy runtime security assurance for agent-based applications;
3. Training and inference technologies for foundation models and samples, ensuring data and model asset security;
4. Secure construction, performance optimization, and of confidential training and inference infrastructure for foundation models.
The base salary range for this position in the selected city is $212800 - $450000 annually.