Research Engineer, AI Safety & Alignment

• Develop and implement novel evaluation methodologies and metrics to assess the safety and alignment of large language models.
• Research and develop cutting-edge techniques for model alignment, value learning, and interpretability.
• Conduct adversarial testing to proactively uncover potential vulnerabilities and failure modes in our models.
• Analyze and mitigate biases, toxicity, and other harmful behaviors in large language models through techniques like reinforcement learning from human feedback (RLHF) and fine-tuning.
• Collaborate with engineering and product teams to translate safety research into practical, scalable solutions and best practices.
• Stay abreast of the latest advancements in AI safety research and contribute to the academic community through publications and presentations.

• Hold a PhD (or equivalent experience) in a relevant field such as Computer Science, Machine Learning, or a related discipline.
• Write clear and clean production-facing and training code
• Experience working with GPUs (training, serving, debugging)
• Experience with data pipelines and data infrastructure
• Strong understanding of modern machine learning techniques, particularly transformers and reinforcement learning, with a focus on their safety implications.
• Are passionate about the responsible development of AI and dedicated to solving complex safety challenges.

• Experience with product experimentation and A/B testing
• Experience training large models in a distributed setting
• Familiarity with ML deployment and orchestration (Kubernetes, Docker, cloud)
• Experience with explainable AI (XAI) and interpretability techniques.
• Have research in AI safety, alignment, ethics, or a related area.
• Knowledge of the broader societal and ethical implications of AI, including policy and governance.
• Publications in relevant academic journals or conferences in the field of machine learning

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