Our Product
Vulcan is a cybersecurity solution specifically designed for GenAI, offering two core services: Red Team (vulnerability assessment) and Blue Team (real-time defense). It ensures GenAI compliance, cybersecurity robustness, and operational integrity.
Since its official launch in 2024, Vulcan has been recognized by the international standard-setting organization OWASP as a certified vendor for LLM & GenAI security testing and assessment. It is one of the few solutions capable of supporting multiple Asian languages (Traditional Chinese, Simplified Chinese, Japanese, Korean, Thai) and Standard Arabic.
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About the role
We are looking for a talented Machine Learning Engineer to join our Product Core Engineering team. You will be responsible for building and optimizing machine learning workflows that directly power our AI-driven products. This role focuses on the full lifecycle of model development — from training and fine-tuning to deployment and monitoring — ensuring robust and efficient ML systems at scale.
Why Join Us?
- Product Impact: Your work will be directly embedded in our core AI products, shaping user experience and product capabilities.
- Engineering Excellence: Be part of a team that values high-quality engineering, reproducibility, and scalability.
- Innovation: Opportunity to experiment with cutting-edge ML and GenAI technologies in production settings.
- Collaboration: Work alongside backend, platform, and product teams in a highly collaborative environment.
- Competitive Package: Receive attractive compensation and benefits aligned with your skills and performance.
Key Responsibilities
- Model Development: Design and implement training processes for machine learning classifiers and generative models.
- Fine-tuning & Prompting: Adapt pre-trained models to specific product needs through fine-tuning, prompt engineering, and parameter optimization.
- Hyperparameter Management: Configure and tune hyperparameters to balance accuracy, robustness, and performance.
- Pipeline Engineering: Build scalable training and evaluation pipelines to support continuous experimentation.
- Integration: Collaborate with backend and product engineers to deploy models into production systems.
- Monitoring & Maintenance: Establish monitoring metrics and retraining strategies to maintain model performance in dynamic environments.
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