Research Engineer, AI Safety & Alignment

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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Relocation assistance

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Schedule

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Weekend work

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Application

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About this role

Research Engineer, AI Safety & Alignment Redwood City, CA, Redwood City, California, United States About the role and team Joining us as a Research Engineer, you'll be at the forefront of tackling one of the most critical challenges in AI today: safety and alignment. Your work will be pivotal in understanding and mitigating the risks of advanced AI, conducting foundational research to make our models safer, and solving the core technical problems of AI alignment -ensuring our models behave in accordance with human values and intentions. The Safety team is dedicated to pioneering and implementing techniques that make our models more robust, honest, and harmless. As a Research Engineer, you will bridge the gap between theoretical research and practical application, writing high-quality code to test hypotheses and integrating successful safety solutions directly into our products. Your research will not only protect millions of users but also contribute to the broader scientific community's understanding of how to build safe, beneficial AI. What you'll do - 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

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