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Your work will directly contribute to making AI systems more trustworthy and aligned with human values, ensuring our models operate safely as they become more capable.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Design and build scalable ML infrastructure to support real-time and batch classifier and safety evaluations across our model ecosystem</li>\n<li>Build monitoring and observability tools to track model performance, data quality, and system health for safety-critical applications</li>\n<li>Collaborate with research teams to productionize safety research, translating experimental safety techniques into robust, scalable systems</li>\n<li>Optimize inference latency and throughput for real-time safety evaluations while maintaining high reliability standards</li>\n<li>Implement automated testing, deployment, and rollback systems for ML models in production safety applications</li>\n<li>Partner with Safeguards, Security, and Alignment teams to understand requirements and deliver infrastructure that meets safety and production needs</li>\n<li>Contribute to the development of internal tools and frameworks that accelerate safety research and deployment</li>\n</ul>\n<p>You may be a good fit if you:</p>\n<ul>\n<li>Have 5+ years of experience building production ML infrastructure, ideally in safety-critical domains like fraud detection, content moderation, or risk assessment</li>\n<li>Are proficient in Python and have experience with ML frameworks like PyTorch, TensorFlow, or JAX</li>\n<li>Have hands-on experience with cloud platforms (AWS, GCP) and container orchestration (Kubernetes)</li>\n<li>Understand distributed systems principles and have built systems that handle high-throughput, low-latency workloads</li>\n<li>Have experience with data engineering tools and building robust data pipelines (e.g., Spark, Airflow, streaming systems)</li>\n<li>Are results-oriented, with a bias towards reliability and impact in safety-critical systems</li>\n<li>Enjoy collaborating with researchers and translating cutting-edge research into production systems</li>\n<li>Care deeply about AI safety and the societal impacts of your work</li>\n</ul>\n<p>Strong candidates may have experience with:</p>\n<ul>\n<li>Working with large language models and modern transformer architectures</li>\n<li>Implementing A/B testing frameworks and experimentation infrastructure for ML systems</li>\n<li>Developing monitoring and alerting systems for ML model performance and data drift</li>\n<li>Building automated labeling systems and human-in-the-loop workflows</li>\n<li>Experience in trust &amp; safety, fraud prevention, or content moderation domains</li>\n<li>Knowledge of privacy-preserving ML techniques and compliance requirements</li>\n<li>Contributing to open-source ML infrastructure projects</li>\n</ul>\n<p>We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_bd9625d9-99b","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://www.anthropic.com/","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/4778843008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$320,000-$405,000 USD","x-skills-required":["Python","PyTorch","TensorFlow","JAX","Cloud platforms (AWS, GCP)","Container orchestration (Kubernetes)","Distributed systems principles","Data engineering tools (Spark, Airflow, streaming systems)"],"x-skills-preferred":["Large language models and modern transformer architectures","A/B testing frameworks and experimentation infrastructure for ML systems","Monitoring and alerting systems for ML model performance and data drift","Automated labeling systems and human-in-the-loop workflows","Trust & safety, fraud prevention, or content moderation domains","Privacy-preserving ML techniques and compliance requirements","Open-source ML infrastructure projects"],"datePosted":"2026-04-18T15:44:06.907Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, PyTorch, TensorFlow, JAX, Cloud platforms (AWS, GCP), Container orchestration (Kubernetes), Distributed systems principles, Data engineering tools (Spark, Airflow, streaming systems), Large language models and modern transformer architectures, A/B testing frameworks and experimentation infrastructure for ML systems, Monitoring and alerting systems for ML model performance and data drift, Automated labeling systems and human-in-the-loop workflows, Trust & safety, fraud prevention, or content moderation domains, Privacy-preserving ML techniques and compliance requirements, Open-source ML infrastructure projects","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":320000,"maxValue":405000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_6cc383e0-ff6"},"title":"ML Infrastructure Engineer, Safeguards","description":"<p><strong>About the role</strong></p>\n<p>We are seeking a Machine Learning Infrastructure Engineer to join our Safeguards organization, where you&#39;ll build and scale the critical infrastructure that powers our AI safety systems. You&#39;ll work at the intersection of machine learning, large-scale distributed systems, and AI safety, developing the platforms and tools that enable our safeguards to operate reliably at scale.</p>\n<p><strong>Responsibilities:</strong></p>\n<ul>\n<li>Design and build scalable ML infrastructure to support real-time and batch classifier and safety evaluations across our model ecosystem</li>\n<li>Build monitoring and observability tools to track model performance, data quality, and system health for safety-critical applications</li>\n<li>Collaborate with research teams to productionize safety research, translating experimental safety techniques into robust, scalable systems</li>\n<li>Optimize inference latency and throughput for real-time safety evaluations while maintaining high reliability standards</li>\n<li>Implement automated testing, deployment, and rollback systems for ML models in production safety applications</li>\n<li>Partner with Safeguards, Security, and Alignment teams to understand requirements and deliver infrastructure that meets safety and production needs</li>\n<li>Contribute to the development of internal tools and frameworks that accelerate safety research and deployment</li>\n</ul>\n<p><strong>You may be a good fit if you:</strong></p>\n<ul>\n<li>Have 5+ years of experience building production ML infrastructure, ideally in safety-critical domains like fraud detection, content moderation, or risk assessment</li>\n<li>Are proficient in Python and have experience with ML frameworks like PyTorch, TensorFlow, or JAX</li>\n<li>Have hands-on experience with cloud platforms (AWS, GCP) and container orchestration (Kubernetes)</li>\n<li>Understand distributed systems principles and have built systems that handle high-throughput, low-latency workloads</li>\n<li>Have experience with data engineering tools and building robust data pipelines (e.g., Spark, Airflow, streaming systems)</li>\n<li>Are results-oriented, with a bias towards reliability and impact in safety-critical systems</li>\n<li>Enjoy collaborating with researchers and translating cutting-edge research into production systems</li>\n<li>Care deeply about AI safety and the societal impacts of your work</li>\n</ul>\n<p><strong>Strong candidates may have experience with:</strong></p>\n<ul>\n<li>Working with large language models and modern transformer architectures</li>\n<li>Implementing A/B testing frameworks and experimentation infrastructure for ML systems</li>\n<li>Developing monitoring and alerting systems for ML model performance and data drift</li>\n<li>Building automated labeling systems and human-in-the-loop workflows</li>\n<li>Experience in trust &amp; safety, fraud prevention, or content moderation domains</li>\n<li>Knowledge of privacy-preserving ML techniques and compliance requirements</li>\n<li>Contributing to open-source ML infrastructure projects</li>\n</ul>\n<p><strong>Deadline to apply:</strong></p>\n<p>None. Applications will be reviewed on a rolling basis.</p>\n<p><strong>Logistics</strong></p>\n<ul>\n<li>Education requirements: We require at least a Bachelor&#39;s degree in a related field or equivalent experience.</li>\n<li>Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.</li>\n<li>Visa sponsorship: We do sponsor visas! However, we aren&#39;t able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.</li>\n</ul>\n<p><strong>We encourage you to apply even if you do not believe you meet every single qualification.</strong></p>\n<p>Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you&#39;re interested in this work.</p>\n<p><strong>Your safety matters to us.</strong></p>\n<p>To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you&#39;re ever unsure about a communication, don&#39;t click any links—visit anthropic.com/careers directly for confirmed position openings.</p>\n<p><strong>How we&#39;re different</strong></p>\n<p>We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing the state of the art in AI safety and making a meaningful difference in the world.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_6cc383e0-ff6","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://job-boards.greenhouse.io","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/4778843008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$320,000 - $405,000 USD","x-skills-required":["Python","PyTorch","TensorFlow","JAX","AWS","GCP","Kubernetes","Spark","Airflow","streaming systems"],"x-skills-preferred":["large language models","modern transformer architectures","A/B testing frameworks","experimentation infrastructure","monitoring and alerting systems","automated labeling systems","human-in-the-loop workflows","trust & safety","fraud prevention","content moderation domains","privacy-preserving ML techniques","compliance requirements"],"datePosted":"2026-03-08T13:46:05.401Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, PyTorch, TensorFlow, JAX, AWS, GCP, Kubernetes, Spark, Airflow, streaming systems, large language models, modern transformer architectures, A/B testing frameworks, experimentation infrastructure, monitoring and alerting systems, automated labeling systems, human-in-the-loop workflows, trust & safety, fraud prevention, content moderation domains, privacy-preserving ML techniques, compliance requirements","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":320000,"maxValue":405000,"unitText":"YEAR"}}}]}