Research Engineer, AI Safety & Alignment
Develops evaluation methods, alignment techniques, and adversarial testing for large language models to ensure safety and alignment with human values. Requires PhD in ML/CS, production code skills, GPU experience, and transformers/RL expertise.
Responsibilities
- 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.
Requirements
- 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.
- Passionate about the responsible development of AI and dedicated to solving complex safety challenges.
Nice to Have
- 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.
- 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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