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You&#39;ll drive strategic initiatives across inference runtime and accelerator performance,coordinating model launches, managing cross-platform dependencies, and ensuring reliability across multiple hardware targets.</p>\n<p>This role is essential for keeping our most contended infrastructure teams shipping effectively while Research, Product, and Safety all depend on their output.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Systems Integration &amp; Coordination: Lead cross-functional initiatives for new infrastructure integration, establishing clear ownership, timelines, and communication channels between teams. Drive end-to-end planning for major infrastructure transitions including platform modernization and new tech adoption.</li>\n</ul>\n<ul>\n<li>Performance &amp; Efficiency: Partner with engineering teams to identify optimisation opportunities, track performance metrics, and prioritise work that unlocks capacity gains. Coordinate across runtime and accelerator layers to ensure efficiency wins ship without compromising reliability.</li>\n</ul>\n<ul>\n<li>Launch Coordination: Drive end-to-end readiness for model and feature launches across multiple hardware platforms. Establish processes for cross-platform validation, manage launch timelines, and ensure smooth handoffs between runtime, accelerator, and downstream teams.</li>\n</ul>\n<ul>\n<li>Strategic Planning: Own and prioritise the inference deployment roadmap, working closely with engineering leadership to prioritise initiatives and manage dependencies. Provide visibility into upcoming changes and their organisational impact.</li>\n</ul>\n<ul>\n<li>Stakeholder Communication: Build strong relationships across research, engineering, and product teams to understand requirements and constraints. 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Establish metrics and dashboards to track infrastructure health, capacity utilisation, and deployment success rates.</li>\n</ul>\n<p>You may be a good fit if you:</p>\n<ul>\n<li>Have several years of experience in technical program management, with proven success delivering complex infrastructure programs, preferably in ML/AI systems or large-scale distributed systems</li>\n</ul>\n<ul>\n<li>Have deep technical understanding of inference systems, compilers, or hardware accelerators to engage substantively with engineers and identify technical risks.</li>\n</ul>\n<ul>\n<li>Excel at creating structure and processes in ambiguous environments, bringing clarity to complex cross-team initiatives</li>\n</ul>\n<ul>\n<li>Have strong stakeholder management skills and can build trust with both technical and non-technical partners</li>\n</ul>\n<ul>\n<li>Are comfortable navigating competing priorities and using data to drive technical decisions</li>\n</ul>\n<ul>\n<li>Have experience with infrastructure scaling initiatives, hardware integrations, or deployment governance</li>\n</ul>\n<ul>\n<li>Thrive in fast-paced environments and can balance strategic planning with tactical execution</li>\n</ul>\n<ul>\n<li>Are passionate about AI infrastructure and understand the unique challenges of deploying and scaling large language models</li>\n</ul>\n<p>Deadline to apply: None, applications will be received on a rolling basis.</p>\n<p>The annual compensation range for this role is $290,000-$365,000 USD.</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_1df66b08-463","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/5107763008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$290,000-$365,000 USD","x-skills-required":["Technical Program Management","Inference Systems","Compilers","Hardware Accelerators","Cross-Functional Initiatives","Infrastructure Integration","Platform Modernization","New Tech Adoption","Performance Metrics","Capacity Gains","Runtime and Accelerator Layers","Efficiency Wins","Reliability","Model and Feature Launches","Cross-Platform Validation","Launch Timelines","Smooth Handoffs","Inference Deployment Roadmap","Engineering Leadership","Prioritisation Initiatives","Dependencies","Upcoming Changes","Organisational Impact","Stakeholder Communication","Requirements and Constraints","Technical Complexities","Leadership Updates","Priorities and Timelines","Process Improvement","Metrics and Dashboards","Infrastructure Health","Capacity Utilisation","Deployment Success Rates"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:54:27.143Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA | Seattle, WA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Technical Program Management, Inference Systems, Compilers, Hardware Accelerators, Cross-Functional Initiatives, Infrastructure Integration, Platform Modernization, New Tech Adoption, Performance Metrics, Capacity Gains, Runtime and Accelerator Layers, Efficiency Wins, Reliability, Model and Feature Launches, Cross-Platform Validation, Launch Timelines, Smooth Handoffs, Inference Deployment Roadmap, Engineering Leadership, Prioritisation Initiatives, Dependencies, Upcoming Changes, Organisational Impact, Stakeholder Communication, Requirements and Constraints, Technical Complexities, Leadership Updates, Priorities and Timelines, Process Improvement, Metrics and Dashboards, Infrastructure Health, Capacity Utilisation, Deployment Success Rates","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":290000,"maxValue":365000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_6f3a053e-c43"},"title":"Staff Software Engineer, AI Reliability Engineering","description":"<p>We&#39;re seeking a Staff Software Engineer to join our AI Reliability Engineering team. 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As a key member of our AIRE (AI Reliability Engineering) team, you will partner with teams across Anthropic to improve reliability across our most critical serving paths. 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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.</p>\n<p><strong>About the Role</strong></p>\n<p>Claude has your back. AIRE has Claude&#39;s. Help us keep Claude reliable for everyone who depends on it.</p>\n<p>AIRE (AI Reliability Engineering) partners with teams across Anthropic to improve reliability across our most critical serving paths -- every hop from the SDK through our network, API layers, serving infrastructure, and accelerators and back. We jump into the trenches alongside partner teams to make the systems that deliver Claude more robust and resilient, be it during an incident or collaborating on projects.</p>\n<p>Reliability here is an emergent phenomenon that transcends any single team&#39;s boundaries, so someone has to zoom out and look at the whole picture. That&#39;s us -- and it means few teams at Anthropic offer this kind of dynamic, cross-cutting exposure to the systems that matter most.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Develop appropriate Service Level Objectives for large language model serving systems, balancing availability and latency with development velocity.</li>\n<li>Design and implement monitoring and observability systems across the token path.</li>\n<li>Assist in the design and implementation of high-availability serving infrastructure across multiple regions and cloud providers</li>\n<li>Lead incident response for critical AI services, ensuring rapid recovery, thorough incident reviews, and systematic improvements.</li>\n<li>Support the reliability of safeguard model serving -- critical for both site reliability and Anthropic&#39;s safety commitments.</li>\n</ul>\n<p><strong>You may be a good fit if you</strong></p>\n<ul>\n<li>Have strong distributed systems, infrastructure, or reliability backgrounds -- we&#39;re looking for reliability-minded software engineers and SREs.</li>\n<li>Are curious and brave -- comfortable jumping into unfamiliar systems during an incident and helping drive resolution even when you don&#39;t have deep expertise yet.</li>\n<li>Think holistically about how systems compose and where the seams are.</li>\n<li>Can build lasting relationships across teams -- our engagement model depends on being welcomed as teammates, not outsiders with opinions.</li>\n<li>Care about users and feel ownership over outcomes, even for systems you don&#39;t own.</li>\n<li>Have excellent communication and collaboration skills -- you&#39;ll be partnering across the entire company.</li>\n<li>Bring diverse experience -- the team&#39;s strength comes from people who&#39;ve built product stacks, scaled databases, run massive distributed systems, and everything in between.</li>\n</ul>\n<p><strong>Strong candidates may also</strong></p>\n<ul>\n<li>Have been an SRE, Production Engineer, or in similar reliability-focused roles on large scale systems</li>\n<li>Have experience operating large-scale model serving or training infrastructure (&gt;1000 GPUs).</li>\n<li>Have experience with one or more ML hardware accelerators (GPUs, TPUs, Trainium).</li>\n<li>Understand ML-specific networking optimizations like RDMA and InfiniBand.</li>\n<li>Have expertise in AI-specific observability tools and frameworks.</li>\n<li>Have experience with chaos engineering and systematic resilience testing.</li>\n<li>Have contributed to open-source infrastructure or ML tooling.</li>\n</ul>\n<p><strong>Logistics</strong></p>\n<p><strong>Education requirements:</strong> We require at least a Bachelor&#39;s degree in a related field or equivalent experience. <strong>Location-based hybrid policy:</strong> 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.</p>\n<p><strong>Visa sponsorship</strong></p>\n<p>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.</p>\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 our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. 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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.</p>\n<p><strong>About the Role</strong></p>\n<p>AIRE (AI Reliability Engineering) partners with teams across Anthropic to improve reliability across our most critical serving paths -- every hop from the SDK through our network, API layers, serving infrastructure, and accelerators and back. We jump into the trenches alongside partner teams to make the systems that deliver Claude more robust and resilient, be it during an incident or collaborating on projects.</p>\n<p>Reliability here is an emergent phenomenon that transcends any single team&#39;s boundaries, so someone has to zoom out and look at the whole picture. That&#39;s us -- and it means few teams at Anthropic offer this kind of dynamic, cross-cutting exposure to the systems that matter most.</p>\n<p>Claude has your back. AIRE has Claude&#39;s. Help us keep Claude reliable for everyone who depends on it.</p>\n<p><strong>Responsibilities:</strong></p>\n<ul>\n<li>Develop appropriate Service Level Objectives for large language model serving systems, balancing availability and latency with development velocity.</li>\n</ul>\n<ul>\n<li>Design and implement monitoring and observability systems across the token path.</li>\n</ul>\n<ul>\n<li>Assist in the design and implementation of high-availability serving infrastructure across multiple regions and cloud providers</li>\n</ul>\n<ul>\n<li>Lead incident response for critical AI services, ensuring rapid recovery, thorough incident reviews, and systematic improvements.</li>\n</ul>\n<ul>\n<li>Support the reliability of safeguard model serving -- critical for both site reliability and Anthropic&#39;s safety commitments.</li>\n</ul>\n<p><strong>You may be a good fit if you:</strong></p>\n<ul>\n<li>Have strong distributed systems, infrastructure, or reliability backgrounds -- we&#39;re looking for reliability-minded software engineers and SREs.</li>\n</ul>\n<ul>\n<li>Are curious and brave -- comfortable jumping into unfamiliar systems during an incident and helping drive resolution even when you don&#39;t have deep expertise yet.</li>\n</ul>\n<ul>\n<li>Think holistically about how systems compose and where the seams are.</li>\n</ul>\n<ul>\n<li>Can build lasting relationships across teams -- our engagement model depends on being welcomed as teammates, not outsiders with opinions.</li>\n</ul>\n<ul>\n<li>Care about users and feel ownership over outcomes, even for systems you don&#39;t own.</li>\n</ul>\n<ul>\n<li>Have excellent communication and collaboration skills -- you&#39;ll be partnering across the entire company.</li>\n</ul>\n<ul>\n<li>Bring diverse experience -- the team&#39;s strength comes from people who&#39;ve built product stacks, scaled databases, run massive distributed systems, and everything in between.</li>\n</ul>\n<p><strong>Strong candidates may also:</strong></p>\n<ul>\n<li>Have been an SRE, Production Engineer, or in similar reliability-focused roles on large scale systems</li>\n</ul>\n<ul>\n<li>Have experience operating large-scale model serving or training infrastructure (&gt;1000 GPUs).</li>\n</ul>\n<ul>\n<li>Have experience with one or more ML hardware accelerators (GPUs, TPUs, Trainium).</li>\n</ul>\n<ul>\n<li>Understand ML-specific networking optimizations like RDMA and InfiniBand.</li>\n</ul>\n<ul>\n<li>Have expertise in AI-specific observability tools and frameworks.</li>\n</ul>\n<ul>\n<li>Have experience with chaos engineering and systematic resilience testing.</li>\n</ul>\n<ul>\n<li>Have contributed to open-source infrastructure or ML tooling.</li>\n</ul>\n<p><strong>Logistics</strong></p>\n<p><strong>Education requirements:</strong> We require at least a Bachelor&#39;s degree in a related field or equivalent experience. <strong>Location-based hybrid policy:</strong> 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.</p>\n<p><strong>Visa sponsorship:</strong> 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.</p>\n<p><strong>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. 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.</strong></p>\n<p><strong>Your safety matters to us. 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.</strong></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 our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. 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You&#39;ll drive strategic initiatives across inference runtime and accelerator performance—coordinating model launches, managing cross-platform dependencies, and ensuring reliability across multiple hardware targets.</p>\n<p><strong>Responsibilities:</strong></p>\n<ul>\n<li><strong>Systems Integration &amp; Coordination</strong>: Lead cross-functional initiatives for new infrastructure integration, establishing clear ownership, timelines, and communication channels between teams. Drive end-to-end planning for major infrastructure transitions including platform modernization and new tech adoption.</li>\n<li><strong>Performance &amp; Efficiency:</strong> Partner with engineering teams to identify optimisation opportunities, track performance metrics, and prioritise work that unlocks capacity gains. 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Translate technical complexities into clear updates for leadership and ensure alignment on priorities and timelines.</li>\n<li><strong>Process Improvement:</strong> Identify inefficiencies in current workflows and drive systematic improvements. Establish metrics and dashboards to track infrastructure health, capacity utilisation, and deployment success rates.</li>\n</ul>\n<p><strong>You may be a good fit if you:</strong></p>\n<ul>\n<li>Have several years of experience in technical program management, with proven success delivering complex infrastructure programs, preferably in ML/AI systems or large-scale distributed systems</li>\n<li>Have deep technical understanding of inference systems, compilers, or hardware accelerators to engage substantively with engineers and identify technical risks.</li>\n<li>Excel at creating structure and processes in ambiguous environments, bringing clarity to complex cross-team initiatives</li>\n<li>Have strong stakeholder management skills and can build trust with both technical and non-technical partners</li>\n<li>Are comfortable navigating competing priorities and using data to drive technical decisions</li>\n<li>Have experience with infrastructure scaling initiatives, hardware integrations, or deployment governance</li>\n<li>Thrive in fast-paced environments and can balance strategic planning with tactical execution</li>\n<li>Are passionate about AI infrastructure and understand the unique challenges of deploying and scaling large language models</li>\n</ul>\n<p><strong>Logistics</strong></p>\n<ul>\n<li><strong>Education requirements:</strong> We require at least a Bachelor&#39;s degree in a related field or equivalent experience.</li>\n<li><strong>Location-based hybrid policy:</strong> Currently, we expect all staff to be in one of our offices at least 25% of the time. 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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>How we&#39;re different</strong></p>\n<p>We believe that the</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_459d7a0d-23e","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/5107763008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$290,000 - $365,000USD","x-skills-required":["technical program management","inference systems","compilers","hardware accelerators","cross-functional initiatives","model launches","cross-platform dependencies","reliability","performance metrics","capacity gains","efficiency wins","runtime","accelerator layers","launch timelines","smooth handoffs","strategic planning","inference deployment roadmap","engineering leadership","prioritisation","dependencies","visibility","upcoming changes","organisational impact","stakeholder communication","requirements","constraints","technical complexities","clear updates","leadership","alignment","priorities","timelines","process improvement","inefficiencies","workflows","systematic improvements","metrics","dashboards","infrastructure health","capacity utilisation","deployment success rates"],"x-skills-preferred":["infrastructure scaling initiatives","hardware integrations","deployment governance","fast-paced environments","strategic planning","tactical execution","AI infrastructure","large language models"],"datePosted":"2026-03-08T13:48:59.030Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA | Seattle, WA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"technical program management, inference systems, compilers, hardware accelerators, cross-functional initiatives, model launches, cross-platform dependencies, reliability, performance metrics, capacity gains, efficiency wins, runtime, accelerator layers, launch timelines, smooth handoffs, strategic planning, inference deployment roadmap, engineering leadership, prioritisation, dependencies, visibility, upcoming changes, organisational impact, stakeholder communication, requirements, constraints, technical complexities, clear updates, leadership, alignment, priorities, timelines, process improvement, inefficiencies, workflows, systematic improvements, metrics, dashboards, infrastructure health, capacity utilisation, deployment success rates, infrastructure scaling initiatives, hardware integrations, deployment governance, fast-paced environments, strategic planning, tactical execution, AI infrastructure, large language models","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":290000,"maxValue":365000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_10798a1e-9fa"},"title":"Staff Software Engineer, AI Reliability Engineering","description":"<p><strong>About Anthropic</strong></p>\n<p>Anthropic&#39;s mission is to create reliable, interpretable, and steerable AI systems. 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We strive to build a team that reflects this perspective, with people from a wide range of backgrounds and disciplines.</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_10798a1e-9fa","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/5101169008","x-work-arrangement":"hybrid","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"€235.000 - €295.000EUR","x-skills-required":["distributed systems","infrastructure","reliability","software engineering","SRE","large scale systems","model serving","training infrastructure","ML hardware accelerators","RDMA","InfiniBand","AI-specific observability tools","chaos engineering","resilience testing","open-source infrastructure","ML tooling"],"x-skills-preferred":["communication","collaboration","diverse experience","product stacks","databases","distributed systems"],"datePosted":"2026-03-08T13:48:18.742Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Dublin"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"distributed systems, infrastructure, reliability, software engineering, SRE, large scale systems, model serving, training infrastructure, ML hardware accelerators, RDMA, InfiniBand, AI-specific observability tools, chaos engineering, resilience testing, open-source infrastructure, ML tooling, communication, collaboration, diverse experience, product stacks, databases, distributed systems"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_7db1bc2d-a0b"},"title":"Senior Audio Video (AV) Engineer","description":"<p>Joining Razer will place you on a global mission to revolutionize the way the world games. Razer is a place to do great work, offering you the opportunity to make an impact globally while working across a global team located across 5 continents. Razer is also a great place to work, providing you the unique, gamer-centric #LifeAtRazer experience that will put you in an accelerated growth, both personally and professionally.</p>\n<p><strong>What you&#39;ll do</strong></p>\n<p>This role focuses on the design and development of screen recording and streaming functionalities on Windows platforms, utilizing advanced audio/video encoding/decoding technologies and hardware accelerators. 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