The future of AI will depend on our ability to keep it safe and responsible, and we're seeking an Applied Machine Learning (ML) Engineer to champion our efforts in doing so.
Requirements
- 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.
- 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.
Responsibilities
- 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.
Other
- Master’s degree in Computer Science, Machine Learning, or a related field or Equivalent.
- 2+ years of experience in AI engineering roles.
- Strong communication skills and a collaborative mindset, with the ability to work effectively in cross-functional teams.
