The Staff+ Software Security Engineer at Anthropic will design and build security systems for AI, focusing on protecting sensitive assets and ensuring the trustworthiness of AI systems.
$405K - $485K / year
598 active roles found
A weekly digest of AI safety, governance, policy and responsible AI roles.
By subscribing, you agree to receive the AI Safety Careers newsletter. We use MailerLite to send emails and may track opens and clicks to improve the newsletter. You can unsubscribe at any time. See our Privacy Policy.
598 active roles found
The Staff+ Software Security Engineer at Anthropic will design and build security systems for AI, focusing on protecting sensitive assets and ensuring the trustworthiness of AI systems.
The Staff+ Software Engineer will develop AI applications for government clients, focusing on scaling products and enhancing public sector operations.
The Staff Software Engineer, Inference at Anthropic will work on building and maintaining systems for AI model inference, focusing on performance optimization and societal impacts.
Get the best AI safety, governance, policy and responsible AI roles in your inbox.
By subscribing, you agree to receive the AI Safety Careers newsletter. We use MailerLite to send emails and may track opens and clicks to improve the newsletter. You can unsubscribe at any time. See our Privacy Policy.
The Staff Research Engineer will work on developing AI systems aimed at scientific discovery, focusing on removing bottlenecks in achieving scientific AGI and creating evaluation frameworks for model capabilities.
The role involves developing systems for safety and oversight of AI models, focusing on misuse prevention and monitoring model behaviors.
The Software Engineer, Safeguards role at Anthropic involves developing systems to monitor AI models, prevent misuse, and ensure user safety.
The Senior Staff Software Engineer, API at Anthropic will lead technical strategy for the Claude API, ensuring its reliability and responsible deployment in AI applications.
The Senior+ Software Engineer for Research Tools at Anthropic will develop infrastructure and applications to support AI safety research, focusing on model evaluation and understanding model behavior.
The Research Scientist, Interpretability role at Anthropic focuses on mechanistic interpretability to enhance the safety and understanding of AI systems.
The role involves conducting research on AI safety and alignment, focusing on understanding and steering the behavior of powerful AI systems.
Research Engineer/Scientist on Anthropic’s Alignment Science team, conducting experimental AI safety research on powerful future systems, safety evaluations, alignment stress-testing, and related safeguards work.
The role focuses on building reliable and interpretable AI systems, emphasizing safety and societal impacts.
The Research Engineer will enhance AI model safety and alignment through post-training techniques, impacting the quality and capabilities of production models.
The Research Engineer will focus on post-training processes to enhance AI model safety and alignment, implementing techniques to improve production model quality.
The Research Engineer will work on training and optimizing large-scale AI models, ensuring their reliability and safety, and addressing production issues.
The Research Engineer will work on training and optimizing production pretrained models, ensuring their reliability and efficiency, with a focus on the societal impacts and safety of AI systems.
The Research Engineer, Pretraining role at Anthropic focuses on developing large language models with an emphasis on safety, alignment, and societal impacts.
The Research Engineer role focuses on advancing the safety and capabilities of large language models through reinforcement learning, collaborating with alignment teams to ensure safe AI systems.
The Research Engineer will redesign how language models interact with external data sources, focusing on safety and societal impacts.
The Research Engineer, Interpretability role at Anthropic focuses on building infrastructure for interpretability research to enhance AI safety through mechanistic understanding of models.