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You&#39;ll prototype new kernel implementations, profile performance across hardware generations, and help define the numerical and parallelism strategies that determine how we scale next-generation AI systems.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Design and implement custom ML kernels (e.g., CUDA, CuTe, Triton) for core LLM operations such as attention, matrix multiplication, gating, and normalization, optimized for modern GPU and accelerator architectures.</li>\n<li>Design and think through compute primitives to reduce memory bandwidth bottlenecks and improve kernel compute efficiency.</li>\n<li>Collaborate with research teams to align kernel-level optimizations with model architecture and algorithmic goals.</li>\n<li>Develop and maintain a library of reusable kernels and performance benchmarks that serve as the foundation for internal model training.</li>\n<li>Contribute to infrastructure stability and scalability, ensuring reproducibility, consistency across precision formats, and high utilization of compute resources.</li>\n<li>Document and share insights through internal talks, technical papers, or open-source contributions to strengthen the broader ML systems community.</li>\n</ul>\n<p><strong>Skills and Qualifications</strong></p>\n<p>Minimum qualifications:</p>\n<ul>\n<li>Bachelor’s degree or equivalent experience in computer science, electrical engineering, statistics, machine learning, physics, robotics, or similar.</li>\n<li>Strong engineering skills, ability to contribute performant, maintainable code and debug in complex codebases</li>\n<li>Understanding of deep learning frameworks (e.g., PyTorch, JAX) and their underlying system architectures.</li>\n<li>Thrive in a highly collaborative environment involving many, different cross-functional partners and subject matter experts.</li>\n<li>A bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships.</li>\n<li>Proficiency in CUDA, CuTe, Triton, or other GPU programming frameworks.</li>\n<li>Demonstrated ability to analyze, profile, and optimize compute-intensive workloads.</li>\n</ul>\n<p>Preferred qualifications:</p>\n<ul>\n<li>Experience training or supporting large-scale language models with tens of billions of parameters or more.</li>\n<li>Track record of improving research productivity through infrastructure design or process improvements.</li>\n<li>Experience developing or tuning kernels for deep learning frameworks such as PyTorch, JAX, or custom accelerators.</li>\n<li>Familiarity with tensor parallelism, pipeline parallelism, or distributed data processing frameworks.</li>\n<li>Experience implementing low-precision formats (FP8, INT8, block floating point) or contributing to related compiler stacks (e.g., XLA, TVM).</li>\n<li>Contributions to open-source GPU, ML systems, or compiler optimization projects.</li>\n<li>Prior research or engineering experience in numerical optimization, communication-efficient training, or scalable AI infrastructure.</li>\n</ul>\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_cba88898-896","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Thinking Machines Lab","sameAs":"https://thinkingmachines.ai/","logo":"https://logos.yubhub.co/thinkingmachines.ai.png"},"x-apply-url":"https://job-boards.greenhouse.io/thinkingmachines/jobs/5013934008","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$350,000 - 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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. Translate technical complexities into clear updates for leadership and ensure alignment on priorities and timelines.</li>\n</ul>\n<ul>\n<li>Process Improvement: 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>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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You will support the reliability of safeguard model serving, which is critical for both site reliability and Anthropic&#39;s safety commitments.</p>\n<p>If you&#39;re committed to creating reliable, interpretable, and steerable AI systems, and you&#39;re passionate about working on complex technical problems, we&#39;d love to hear from you.</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_6f3a053e-c43","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/5101169008","x-work-arrangement":"hybrid","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"€235.000-€295.000 EUR","x-skills-required":["distributed systems","infrastructure","reliability","Service Level Objectives","monitoring","observability","incident response","high-availability serving infrastructure","cloud providers"],"x-skills-preferred":["SRE","Production Engineer","chaos engineering","systematic resilience testing","AI-specific observability tools and frameworks","ML hardware accelerators","RDMA","InfiniBand"],"datePosted":"2026-04-18T15:53:59.220Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Dublin, IE"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"distributed systems, infrastructure, reliability, Service Level Objectives, monitoring, observability, incident response, high-availability serving infrastructure, cloud providers, SRE, Production Engineer, chaos engineering, systematic resilience testing, AI-specific observability tools and frameworks, ML hardware accelerators, RDMA, InfiniBand"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_709b405a-48b"},"title":"Staff / Senior Software Engineer, AI Reliability","description":"<p>We&#39;re seeking a Staff / Senior Software Engineer, AI Reliability to join our team. 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. You will develop Service Level Objectives for large language model serving systems, design and implement monitoring and observability systems, assist in the design and implementation of high-availability serving infrastructure, lead incident response for critical AI services, and support the reliability of safeguard model serving.</p>\n<p>You may be a good fit for this role if you have strong distributed systems, infrastructure, or reliability backgrounds, are curious and brave, think holistically about how systems compose and where the seams are, can build lasting relationships across teams, care about users and feel ownership over outcomes, have excellent communication and collaboration skills, and bring diverse experience.</p>\n<p>Strong candidates may also have experience operating large-scale model serving or training infrastructure, experience with one or more ML hardware accelerators, understanding of ML-specific networking optimizations, expertise in AI-specific observability tools and frameworks, experience with chaos engineering and systematic resilience testing, and contributions to open-source infrastructure or ML tooling.</p>\n<p>We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. 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Experience</strong></p>\n<ul>\n<li>Degree (BA required, MS or PhD preferred) in Computer Science, Machine Learning, or a related field</li>\n<li>Strong software engineering skills with a proven track record of building complex systems</li>\n<li>Expertise in Python and deep learning frameworks</li>\n<li>Have worked on high-performance, large-scale ML systems, particularly in the context of language modelling</li>\n<li>Familiarity with ML Accelerators, Kubernetes, and large-scale data processing</li>\n<li>Strong problem-solving skills and a results-oriented mindset</li>\n<li>Excellent communication skills and ability to work in a collaborative environment</li>\n</ul>\n<p><strong>You&#39;ll thrive in this role if you</strong></p>\n<ul>\n<li>Have significant software engineering experience</li>\n<li>Are able to balance research goals with practical engineering constraints</li>\n<li>Are happy to take on tasks outside your job description to support the team</li>\n<li>Enjoy pair programming and collaborative work</li>\n<li>Are eager to learn more about machine learning research</li>\n<li>Are enthusiastic to work at an organisation that functions as a single, cohesive team pursuing large-scale AI research projects</li>\n<li>Have ambitious goals for AI safety and general progress in the next few years, and you’re excited to create the best outcomes over the long-term</li>\n</ul>\n<p><strong>Sample Projects</strong></p>\n<ul>\n<li>Optimising the throughput of novel attention mechanisms</li>\n<li>Proposing Transformer variants, and experimentally comparing their performance</li>\n<li>Preparing large-scale datasets for model consumption</li>\n<li>Scaling distributed training jobs to thousands of accelerators</li>\n<li>Designing fault tolerance strategies for training infrastructure</li>\n<li>Creating interactive visualisations of model internals, such as attention patterns</li>\n</ul>\n<p><strong>Benefits</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. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We&#39;re an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.</p>\n<p><strong>Come work with us!</strong></p>\n<p>Anthropic is a public benefit corporation headquartered in San Francisco. 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As a technical expert, you will lead a small, high-impact team to drive advances in machine learning acceleration. Your primary responsibilities will include direct technical contribution, technical team leadership, architectural alignment, HW-SW strategy, and execution management.</p>\n<p>You will spend a significant portion of your time on technical execution while managing a multi-disciplinary team to evolve our software stack. You will directly contribute to the codebase and technical strategy, focusing on acting as a Mountain View-based bridge between our co-design time and the Gemini core team.</p>\n<p>You will lead a small team of ML software engineers across numerics, performance optimization, novel training techniques, and novel model exploration. 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We are committed to equal employment opportunity regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy, or related condition (including breastfeeding) or any other basis as protected by applicable law.</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_6be97e03-e54","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Google DeepMind","sameAs":"https://deepmind.com/","logo":"https://logos.yubhub.co/deepmind.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/deepmind/jobs/7509867","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["high-performance software","AI/ML","technical leadership","architectural alignment","HW-SW strategy","execution management"],"x-skills-preferred":["Master's or Ph.D. in a related field","hands-on experience with high-performance compute IPs (GPUs, ML accelerators)","experience contributing to silicon development","expertise in at least one core silicon engineering discipline (e.g., RTL, PD, DV) and familiarity with the full ASIC flow"],"datePosted":"2026-04-18T15:42:14.076Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Mountain View, California, US"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"high-performance software, AI/ML, technical leadership, architectural alignment, HW-SW strategy, execution management, Master's or Ph.D. in a related field, hands-on experience with high-performance compute IPs (GPUs, ML accelerators), experience contributing to silicon development, expertise in at least one core silicon engineering discipline (e.g., RTL, PD, DV) and familiarity with the full ASIC flow"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_1507524b-770"},"title":"Research Engineer, Performance RL","description":"<p>We&#39;re hiring a Research Engineer to join our Code RL team within the RL organization. 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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 team</strong></p>\n<p>We are seeking passionate Research Scientists and Engineers to join our growing Pre-training team in Zurich. We are involved in developing the next generation of large language models. The team primarily focuses on multimodal capabilities: giving LLMs the ability to understand and interact with modalities other than text.</p>\n<p>In this role, you will work at the intersection of cutting-edge research and practical engineering, contributing to the development of safe, steerable, and trustworthy AI systems.</p>\n<p><strong>Responsibilities</strong></p>\n<p>In this role you will interact with many parts of the engineering and research stacks.</p>\n<ul>\n<li>Conduct research and implement solutions in areas such as model architecture, algorithms, data processing, and optimizer development</li>\n</ul>\n<ul>\n<li>Independently lead small research projects while collaborating with team members on larger initiatives</li>\n</ul>\n<ul>\n<li>Design, run, and analyse scientific experiments to advance our understanding of large language models</li>\n</ul>\n<ul>\n<li>Optimise and scale our training infrastructure to improve efficiency and reliability</li>\n</ul>\n<ul>\n<li>Develop and improve dev tooling to enhance team productivity</li>\n</ul>\n<ul>\n<li>Contribute to the entire stack, from low-level optimisations to high-level model design</li>\n</ul>\n<p><strong>Qualifications &amp; Experience</strong></p>\n<p>We encourage you to apply even if you do not believe you meet every single criterion. Because we focus on so many areas, the team is looking for both experienced engineers and strong researchers, and encourage anyone along the researcher/engineer spectrum to apply.</p>\n<ul>\n<li>Degree (BA required, MS or PhD preferred) in Computer Science, Machine Learning, or a related field</li>\n</ul>\n<ul>\n<li>Strong software engineering skills with a proven track record of building complex systems</li>\n</ul>\n<ul>\n<li>Expertise in Python and deep learning frameworks</li>\n</ul>\n<ul>\n<li>Have worked on high-performance, large-scale ML systems, particularly in the context of language modelling</li>\n</ul>\n<ul>\n<li>Familiarity with ML Accelerators, Kubernetes, and large-scale data processing</li>\n</ul>\n<ul>\n<li>Strong problem-solving skills and a results-oriented mindset</li>\n</ul>\n<ul>\n<li>Excellent communication skills and ability to work in a collaborative environment</li>\n</ul>\n<p><strong>You&#39;ll thrive in this role if you</strong></p>\n<ul>\n<li>Have significant software engineering experience</li>\n</ul>\n<ul>\n<li>Are able to balance research goals with practical engineering constraints</li>\n</ul>\n<ul>\n<li>Are happy to take on tasks outside your job description to support the team</li>\n</ul>\n<ul>\n<li>Enjoy pair programming and collaborative work</li>\n</ul>\n<ul>\n<li>Are eager to learn more about machine learning research</li>\n</ul>\n<ul>\n<li>Are enthusiastic to work at an organisation that functions as a single, cohesive team pursuing large-scale AI research projects</li>\n</ul>\n<ul>\n<li>Have ambitious goals for AI safety and general progress in the next few years, and you’re excited to create the best outcomes over the long-term</li>\n</ul>\n<p><strong>Sample Projects</strong></p>\n<ul>\n<li>Optimising the throughput of novel attention mechanisms</li>\n</ul>\n<ul>\n<li>Proposing Transformer variants, and experimentally comparing their performance</li>\n</ul>\n<ul>\n<li>Preparing large-scale datasets for model consumption</li>\n</ul>\n<ul>\n<li>Scaling distributed training jobs to thousands of accelerators</li>\n</ul>\n<ul>\n<li>Designing fault tolerance strategies for training infrastructure</li>\n</ul>\n<ul>\n<li>Creating interactive visualisations of model internals, such as attention patterns</li>\n</ul>\n<p>If you&#39;re excited about pushing the boundaries of AI while prioritising safety and ethics, we want to hear from you!</p>\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>How we&#39;re different</strong></p>\n<p>We believe that the highest-impact work in AI safety and general progress in the next few years will be done by a single, cohesive team pursuing large-scale AI research projects. We&#39;re committed to creating a work environment that is inclusive, diverse, and supportive of our team members&#39; well-being and career growth.</p>\n<p><strong>Career Growth</strong></p>\n<p>We&#39;re committed to helping our team members grow and develop their careers. We offer opportunities for professional development, mentorship, and career advancement. We believe that our team members are the key to our success, and we&#39;re committed to supporting their growth and development.</p>\n<p><strong>Benefits</strong></p>\n<p>We offer a competitive salary and benefits package, including health insurance, retirement savings, and paid time off. We also offer a range of perks, including a generous parental leave policy, flexible work arrangements, and access to cutting-edge technology and tools.</p>\n<p><strong>How to Apply</strong></p>\n<p>If you&#39;re excited about joining our team and contributing to the development of safe, steerable, and trustworthy AI systems, please submit your application. We can&#39;t wait to hear from you!</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_e4704a60-8d4","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/5135168008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"CHF280,000 - CHF680,000","x-skills-required":["Python","Deep learning frameworks","Machine learning","Software engineering","Kubernetes","ML Accelerators","Large-scale data processing"],"x-skills-preferred":["Transformer variants","Attention mechanisms","Distributed training jobs","Fault tolerance strategies","Interactive visualisations"],"datePosted":"2026-03-08T13:58:52.040Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Zürich"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, Deep learning frameworks, Machine learning, Software engineering, Kubernetes, ML Accelerators, Large-scale data processing, Transformer variants, Attention mechanisms, Distributed training jobs, Fault tolerance strategies, Interactive visualisations","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":280000,"maxValue":680000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_73ff6f07-c0e"},"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. 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. We view AI research as an empirical science, which has as much in common with physics and engineering as it does with computer science.</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_73ff6f07-c0e","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/5101173008","x-work-arrangement":"hybrid","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"£325,000 - £390,000GBP","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":["SRE","Production Engineer","reliability-focused roles","ML hardware accelerators","RDMA","InfiniBand","AI-specific observability tools","chaos engineering","resilience testing","open-source infrastructure","ML tooling"],"datePosted":"2026-03-08T13:51:34.354Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"London, UK"}},"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, SRE, Production Engineer, reliability-focused roles, ML hardware accelerators, RDMA, InfiniBand, AI-specific observability tools, chaos engineering, resilience testing, open-source infrastructure, ML tooling","baseSalary":{"@type":"MonetaryAmount","currency":"GBP","value":{"@type":"QuantitativeValue","minValue":325000,"maxValue":390000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_9c72720b-6af"},"title":"Research Engineer, Science of Scaling","description":"<p><strong>About Anthropic</strong></p>\n<p>Anthropic&#39;s mission 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.</p>\n<p><strong>About the role</strong></p>\n<p>Anthropic is seeking a Research Engineer/Scientist to join the Science of Scaling team, responsible for developing the next generation of large language models. In this role, you will work at the intersection of cutting-edge research and practical engineering, contributing to the development of safe, steerable, and trustworthy AI systems. You&#39;ll contribute across the entire stack, from low-level optimizations to high-level algorithm and experimental design, balancing research goals with practical engineering constraints.</p>\n<p><strong>Responsibilities:</strong></p>\n<ul>\n<li>Conduct research into the science of converting compute into intelligence</li>\n<li>Independently lead small research projects while collaborating with team members on larger initiatives</li>\n<li>Design, run, and analyse scientific experiments to advance our understanding of large language models</li>\n<li>Optimise training infrastructure to improve efficiency and reliability</li>\n<li>Develop dev tooling to enhance team productivity</li>\n</ul>\n<p><strong>You may be a good fit if you:</strong></p>\n<ul>\n<li>Have significant software engineering experience and a proven track record of building complex systems</li>\n<li>Hold an advanced degree (MS or PhD) in Computer Science, Machine Learning, or a related field</li>\n<li>Are proficient in Python and experienced with deep learning frameworks</li>\n<li>Are results-oriented with a bias towards flexibility and impact</li>\n<li>Enjoy pair programming and collaborative work, and are willing to take on tasks outside your job description to support the team</li>\n<li>View research and engineering as two sides of the same coin, seeking to understand all aspects of the research program to maximise impact</li>\n<li>Care about the societal impacts of your work and have ambitious goals for AI safety and general progress</li>\n</ul>\n<p><strong>Strong candidates may have:</strong></p>\n<ul>\n<li>Experience with JAX</li>\n<li>Experience with reinforcement learning</li>\n<li>Experience working on high-performance, large-scale ML systems</li>\n<li>Familiarity with accelerators, Kubernetes, and OS internals</li>\n<li>Experience with language modeling using transformer architectures</li>\n<li>Background in large-scale ETL processes</li>\n<li>Experience with distributed training at scale (thousands of accelerators)</li>\n</ul>\n<p><strong>Strong candidates need not have:</strong></p>\n<ul>\n<li>Experience in all of the above areas — we value breadth of interest and willingness to learn over checking every box</li>\n<li>Prior work specifically on language models or transformers; strong engineering fundamentals and ML knowledge transfer well</li>\n<li>An advanced degree — exceptional engineers with strong research instincts are equally encouraged to apply</li>\n</ul>\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. 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. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We&#39;re an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.</p>\n<p>The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including</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_9c72720b-6af","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/5126127008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"£260,000 - £630,000GBP","x-skills-required":["software engineering","Python","deep learning frameworks","JAX","reinforcement learning","high-performance, large-scale ML systems","accelerators","Kubernetes","OS internals","language modeling using transformer architectures","large-scale ETL processes","distributed training at scale"],"x-skills-preferred":["JAX","reinforcement learning","high-performance, large-scale ML systems","accelerators","Kubernetes","OS internals","language modeling using transformer architectures","large-scale ETL processes","distributed training at scale"],"datePosted":"2026-03-08T13:50:55.750Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"London, UK"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"software engineering, Python, deep learning frameworks, JAX, reinforcement learning, high-performance, large-scale ML systems, accelerators, Kubernetes, OS internals, language modeling using transformer architectures, large-scale ETL processes, distributed training at scale, JAX, reinforcement learning, high-performance, large-scale ML systems, accelerators, Kubernetes, OS internals, language modeling using transformer architectures, large-scale ETL processes, distributed training at scale","baseSalary":{"@type":"MonetaryAmount","currency":"GBP","value":{"@type":"QuantitativeValue","minValue":260000,"maxValue":630000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_c930b80e-7a6"},"title":"Staff / Senior Software Engineer, AI Reliability","description":"<p><strong>About Anthropic</strong></p>\n<p>Anthropic&#39;s mission 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.</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. We view AI research as a team sport, where everyone contributes to the overall success of the team.</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_c930b80e-7a6","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/5113224008","x-work-arrangement":"hybrid","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"$325,000 - $485,000 USD","x-skills-required":["distributed systems","infrastructure","reliability","large language model serving systems","monitoring and observability systems","high-availability serving infrastructure","incident response","safeguard model serving"],"x-skills-preferred":["SRE","Production Engineer","ML hardware accelerators","ML-specific networking optimizations","AI-specific observability tools and frameworks","chaos engineering","systematic resilience testing","open-source infrastructure or ML tooling"],"datePosted":"2026-03-08T13:50:54.182Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA | New York City, NY | Seattle, WA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"distributed systems, infrastructure, reliability, large language model serving systems, monitoring and observability systems, high-availability serving infrastructure, incident response, safeguard model serving, SRE, Production Engineer, ML hardware accelerators, ML-specific networking optimizations, AI-specific observability tools and frameworks, chaos engineering, systematic resilience testing, open-source infrastructure or ML tooling","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":325000,"maxValue":485000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_25934fbc-c50"},"title":"Staff / Senior Software Engineer, Cloud Inference","description":"<p><strong>About the Role</strong></p>\n<p>The Cloud Inference team scales and optimizes Claude to serve the massive audiences of developers and enterprise companies across AWS, GCP, Azure, and future cloud service providers (CSPs). We own the end-to-end product of Claude on each cloud platform—from API integration and intelligent request routing to inference execution, capacity management, and day-to-day operations.</p>\n<p>Our engineers are extremely high leverage: we simultaneously drive multiple major revenue streams while optimizing one of Anthropic&#39;s most precious resources—compute. As we expand to more cloud platforms, the complexity of managing inference efficiently across providers with different hardware, networking stacks, and operational models grows significantly. We need engineers who can navigate these platform differences, build robust abstractions that work across providers, and make smart infrastructure decisions that keep us cost-effective at massive scale.</p>\n<p>Your work will increase the scale at which our services operate, accelerate our ability to reliably launch new frontier models and innovative features to customers across all platforms, and ensure our LLMs meet rigorous safety, performance, and security standards.</p>\n<p><strong>What You&#39;ll Do</strong></p>\n<ul>\n<li>Design and build infrastructure that serves Claude across multiple CSPs, accounting for differences in compute hardware, networking, APIs, and operational models</li>\n<li>Collaborate with CSP partner engineering teams to resolve operational issues, influence provider roadmaps, and stand up end-to-end serving on new cloud platforms</li>\n<li>Design and evolve CI/CD automation systems, including validation and deployment pipelines, that reliably ship new model versions to millions of users across cloud platforms without regressions</li>\n<li>Design interfaces and tooling abstractions across CSPs that enable cost-effective inference management, scale across providers, and reduce per-platform complexity</li>\n<li>Contribute to capacity planning and autoscaling strategies that dynamically match supply with demand across CSP validation and production workloads</li>\n<li>Optimize inference cost and performance across providers—designing workload placement and routing systems that direct requests to the most cost-effective accelerator and region</li>\n<li>Contribute to inference features that must work consistently across all platforms</li>\n<li>Analyze observability data across providers to identify performance bottlenecks, cost anomalies, and regressions, and drive remediation based on real-world production workloads</li>\n</ul>\n<p><strong>You May Be a Good Fit If You:</strong></p>\n<ul>\n<li>Have significant software engineering experience, with a strong background in high-performance, large-scale distributed systems serving millions of users</li>\n<li>Have experience building or operating services on at least one major cloud platform (AWS, GCP, or Azure), with exposure to Kubernetes, Infrastructure as Code or container orchestration</li>\n<li>Have strong interest in inference</li>\n<li>Thrive in cross-functional collaboration with both internal teams and external partners</li>\n<li>Are a fast learner who can quickly ramp up on new technologies, hardware platforms, and provider ecosystems</li>\n<li>Are highly autonomous and self-driven, taking ownership of problems end-to-end with a bias toward flexibility and high-impact work</li>\n<li>Pick up slack, even when it goes outside your job description</li>\n</ul>\n<p><strong>Strong Candidates May Also Have Experience With</strong></p>\n<ul>\n<li>Direct experience working with CSP partner teams to scale infrastructure or products across multiple platforms, navigating differences in networking, security, privacy, billing, and managed service offerings</li>\n<li>A background in building platform-agnostic tooling or abstraction layers that work across cloud providers</li>\n<li>Hands-on experience with capacity management, cost optimization, or resource planning at scale across heterogeneous environments</li>\n<li>Strong familiarity with LLM inference optimization, batching, caching, and serving strategies</li>\n<li>Experience with Machine learning infrastructure including GPUs, TPUs, Trainium, or other AI accelerators</li>\n<li>Background designing and building CI/CD systems that automate deployment and validation across cloud environments</li>\n<li>Solid understanding of multi-region deployments, geographic routing, and global traffic management</li>\n<li>Proficiency in Python or Rust</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 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_25934fbc-c50","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/5107466008","x-work-arrangement":"hybrid","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"$300,000 - $485,000 USD","x-skills-required":["Software engineering","Cloud infrastructure","Kubernetes","Infrastructure as Code","Container orchestration","LLM inference optimization","Batching","Caching","Serving strategies","Machine learning infrastructure","GPUs","TPUs","Trainium","AI accelerators","CI/CD systems","Deployment and validation","Cloud environments","Multi-region deployments","Geographic routing","Global traffic management"],"x-skills-preferred":["Python","Rust","Cloud platforms","Networking","Security","Privacy","Billing","Managed service offerings","Platform-agnostic tooling","Abstraction layers","Capacity management","Cost optimization","Resource planning"],"datePosted":"2026-03-08T13:49:59.956Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA | Seattle, WA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Software engineering, Cloud infrastructure, Kubernetes, Infrastructure as Code, Container orchestration, LLM inference optimization, Batching, Caching, Serving strategies, Machine learning infrastructure, GPUs, TPUs, Trainium, AI accelerators, CI/CD systems, Deployment and validation, Cloud environments, Multi-region deployments, Geographic routing, Global traffic management, Python, Rust, Cloud platforms, Networking, Security, Privacy, Billing, Managed service offerings, Platform-agnostic tooling, Abstraction layers, Capacity management, Cost optimization, Resource planning","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":300000,"maxValue":485000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_459d7a0d-23e"},"title":"Technical Program Manager, Inference Performance","description":"<p>As a Technical Program Manager for Inference, you&#39;ll be the critical bridge between our inference systems and the broader organisation. 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. Coordinate across runtime and accelerator layers to ensure efficiency wins ship without compromising reliability.</li>\n<li><strong>Launch Coordination:</strong> 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<li><strong>Strategic Planning:</strong> 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<li><strong>Stakeholder Communication:</strong> Build strong relationships across research, engineering, and product teams to understand requirements and constraints. 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. However, some roles may require more time in our offices.</li>\n<li><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.</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. 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>Salary</strong></p>\n<p>The annual compensation range for this role is €235.000 - €295.000EUR.</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. We view AI research as an empirical science, which has as much in common with physics and engineering as it does with computer science. 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_7badeaf5-492"},"title":"Hardware / Software CoDesign Engineer","description":"<p><strong>Hardware / Software CoDesign Engineer</strong></p>\n<p><strong>Location</strong></p>\n<p>San Francisco</p>\n<p><strong>Employment Type</strong></p>\n<p>Full time</p>\n<p><strong>Location Type</strong></p>\n<p>Hybrid</p>\n<p><strong>Department</strong></p>\n<p>Scaling</p>\n<p><strong>Compensation</strong></p>\n<ul>\n<li>$342K – $555K • Offers Equity</li>\n</ul>\n<p>The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. 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You will evangelize these constraints with various vendors to develop and influence future hardware architectures towards efficient training and inference on our models. If you are excited about efficiently distributing a large language model across devices, dealing with and optimizing system-wide/rack-wide networking bottlenecks and eventually tailoring the compute pipe and memory hierarchy of the hardware platform, simulating workloads at different abstractions and working closely with our partners, this is the perfect opportunity!</p>\n<p><strong>In this role, you will:</strong></p>\n<ul>\n<li>Co-design future hardware for programmability and performance with our hardware vendors</li>\n</ul>\n<ul>\n<li>Assist hardware vendors in developing optimal kernels and add support for it in our compiler</li>\n</ul>\n<ul>\n<li>Develop performance estimates for critical kernels for different hardware configurations and drive decisions on compute core and memory hierarchy features</li>\n</ul>\n<ul>\n<li>Build system performance models at different abstraction levels and carry out analysis to drive decisions on scale up, scale out, front end networking</li>\n</ul>\n<ul>\n<li>Work with machine learning engineers, kernel engineers and compiler developers to understand their vision and needs from high performance accelerators</li>\n</ul>\n<ul>\n<li>Manage communication and coordination with internal and external partners</li>\n</ul>\n<ul>\n<li>Influence the roadmap of hardware partners to optimize them for OpenAI’s workloads.</li>\n</ul>\n<ul>\n<li>Evaluate potential partners’ accelerators and platforms.</li>\n</ul>\n<ul>\n<li>As the scope of the role and team grows, understand and influence roadmaps for hardware partners for our datacenter networks, racks, and buildings.</li>\n</ul>\n<p><strong>You might thrive in this role if you have:</strong></p>\n<ul>\n<li>4+ years of industry experience, including experience harnessing compute at scale and optimizing ML platform code to run efficiently on target hardware.</li>\n</ul>\n<ul>\n<li>Strong experience in software/hardware co-design</li>\n</ul>\n<ul>\n<li>Deep understanding of GPU and/or other AI accelerators</li>\n</ul>\n<ul>\n<li>Experience with CUDA, Triton or a related accelerator programming language</li>\n</ul>\n<ul>\n<li>Experience driving Machine Learning accuracy with low precision formats</li>\n</ul>\n<ul>\n<li>Experience with system performance modeling and analysis to optimize ML model deployment</li>\n</ul>\n<ul>\n<li>Strong coding skills in C/C++ and Python</li>\n</ul>\n<ul>\n<li>Are familiar with the fundamentals of deep learning computing and chip architecture/microarchitecture.</li>\n</ul>\n<p><strong>These attributes are nice to have:</strong></p>\n<ul>\n<li>PhD in Computer Science and Engineering with a specialization in Computer Architecture, Parallel Computing. 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