Job Details

Virtue AI is at the forefront of AI security. As enterprises increasingly adopt Large Language Models, the need for robust, trustworthy, and safe AI has never been greater. Our mission is to build the essential guardrails and red-teaming tools that enable organizations to deploy multi-modal AI applications confidently and responsibly.

We are a well-funded, early-stage startup founded by industry veterans, and we're looking for passionate builders to join our core team. Are you a high-performing, motivated engineer ready to make a significant impact in the AI security space? Virtue AI is seeking talented AI Infrastructure Engineer (MLOps) to join us.

About The Role

The future of AI will depend on our ability to keep it safe and responsible. We're seeking an Applied Machine Learning (ML) Engineer to champion our efforts in doing so. You will play a pivotal role in building robust ML systems to address AI safety challenges. You will combine cutting-edge machine learning techniques with strong engineering practices to design and deploy scalable, effective solutions that detect and mitigate risks in AI systems.

In this role, you will:

• Develop AI Risk Mitigation Systems: Design, build, and deploy scalable ML models and workflows to detect, analyze, and mitigate threats to AI/ML environments
• End-to-End ML Workflow Ownership: Implement experimentation pipelines, model evaluation strategies, and deployment mechanisms to productionize AI safety tools
• Red-Teaming and Testing: Facilitate red-teaming exercises to uncover vulnerabilities, validate robustness, and enhance the reliability of AI models
• Collaborate Across Teams: Work closely with researchers, engineers, and security experts to ensure that technical solutions align with product goals and safety objectives

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Machine Learning, or a related field.
• 2+ years of experience in applied machine learning or AI engineering roles.
• Proficiency in Python and ML frameworks, with a focus on Hugging Face Transformers and PyTorch.
• Hands-on experience with fine-tuning pipelines for large language models (e.g., Qwen, LLaMA) and generative image/video models.
• Solid understanding of model architectures, including knowledge of transformer-based models and neural network design.
• Expertise in model inference and deployment frameworks, including optimizing and scaling ML systems in production environments.
• Experience with prompt engineering and developing effective strategies for generative AI systems.
• Strong communication skills and a collaborative mindset, with the ability to work effectively in cross-functional teams.

Preferred Qualifications:

• Experience with AI/ML safety, security, or adversarial machine learning in text, image, video, or audio domains.
• Knowledge of secure software practices and AI vulnerability testing.
• Familiarity with tools and frameworks for red-teaming in AI, particularly for generative models.