Virtue AI | Applied Machine Learning Engineer

About the RoleThe 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. Working at the intersection of AI and security, you will help shape the future of safe AI 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 AIML 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 such as PyTorch, TensorFlow, or Hugging Face Transformers.Familiarity with large language models (e.g., GPT, LLaMA) and generative imagevideo models.Ability to implement end-to-end ML workflows, from data processing to deployment.Excellent communication skills and a collaborative mindset.Preferred Qualifications:Experience with AIML 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.Experience fine-tuning and evaluating LLMs and generative models for safety-critical applications.

Created: 2025-01-03