Machine Learning Engineer, Vulcan

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.

Learn more about us 👉

• Vulcan product: https://vulcanlab.ai/
• Vulcan LinkedIn: https://www.linkedin.com/company/vulcanlab-ai/
• AIFT group: https://aift.io/
• Tech Blog: https://medium.com/onedegree-tech-blog

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.
  

How to Apply

Please apply this position through 👉 https://job-boards.greenhouse.io/aift/jobs/5652688004

It will help us process your applications faster!!

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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• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field.
• Proven experience in training classifiers and fine-tuning transformer-based models.
• Strong understanding of tokenization techniques, embedding models, and vector representations.
• Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, or Hugging Face Transformers.
• Familiarity with hyperparameter tuning and model configuration best practices.
• Experience working with large-scale datasets and building reproducible ML pipelines.

Desired Skills

• Knowledge of cloud platforms (AWS, Azure, GCP) and containerized deployment (Docker, Kubernetes).
• Experience with LLM prompting and fine-tuning is a plus.
• Basic understanding of data engineering practices (ETL, data validation, feature engineering).
• Exposure to AI safety, security, or bias mitigation techniques is an advantage.

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• Phone interview: 1 hour meet with HR
• Onsite Interview: 2~3 hours
  • 2-2.5 hours meet with our team and hiring managers
  • catch up with HR