Anthropicmission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.Anthropic's production models undergo sophisticated post-training processes to enhance their capabilities, alignment, and safety. As a Research Engineer on our Post-Training team, you'll train our base models through the complete post-training stack to deliver the production Claude models that users interact with.You'll work at the intersection of cutting-edge research and production engineering, implementing, scaling, and improving post-training techniques like Constitutional AI, RLHF, and other alignment methodologies. Your work will directly impact the quality, safety, and capabilities of our production models.Note: For this role, we conduct all interviews in Python. This role may require responding to incidents on short-notice, including on weekends.Responsibilities:Implement and optimize post-training techniques at scale on frontier modelsConduct research to develop and optimize post-training recipes that directly improve production model qualityDesign, build, and run robust, efficient pipelines for model fine-tuning and evaluationDevelop tools to measure and improve model performance across various dimensionsCollaborate with research teams to translate emerging techniques into production-ready implementationsDebug complex issues in training pipelines and model behaviorHelp establish best practices for reliable, reproducible model post-trainingYou may be a good fit if you:Thrive in controlled chaos and are energised, rather than overwhelmed, when juggling multiple urgent prioritiesAdapt quickly to changing prioritiesMaintain clarity when debugging complex, time-sensitive issuesHave strong software engineering skills with experience building complex ML systemsAre comfortable working with large-scale distributed systems and high-performance computingHave experience with training, fine-tuning, or evaluating large language modelsCan balance research exploration with engineering rigor and operational reliabilityAre adept at analyzing and debugging model training processesEnjoy collaborating across research and engineering disciplinesCan navigate ambiguity and make progress in fast-moving research environmentsStrong candidates may also:Have experience with LLMsHave a keen interest in AI safety and responsible deploymentWe welcome candidates at various experience levels, with a preference for senior engineers who have hands-on experience with frontier AI systems. However, proficiency in Python, deep learning frameworks, and distributed computing is required for this role.The annual compensation range for this role is listed below.For sales roles, the range provided is the roleTarget Earnings (\