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YubHub-native raw fields carry `x-` prefix.","jobs":[{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_c85087bb-6a8"},"title":"AI Tutor - Danish","description":"<p>As an AI Tutor specialized in multilingual audio capabilities, you will contribute to xAI&#39;s mission by training and refining Grok to excel in voice interactions, speech recognition, and auditory experiences across diverse languages, accents, and cultural contexts.</p>\n<p>Your work will focus on curating and annotating high-quality audio data to enhance Grok&#39;s global accessibility, enabling natural spoken interactions for users worldwide, bridging language barriers through accurate speech processing, and improving the AI&#39;s handling of multilingual audio nuances.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Use proprietary software to provide labels, annotations, recordings, and inputs on projects involving multilingual audio clips, voice recordings, speech samples, and auditory elements in various languages.</li>\n</ul>\n<ul>\n<li>Support the delivery of high-quality curated audio data that ensures clear, natural spoken output, accurate representation of linguistic and prosodic details (such as intonation, rhythm, and accent), and professional audio standards.</li>\n</ul>\n<ul>\n<li>Collaborate with technical staff to develop tasks that improve AI&#39;s ability to handle speech modulation, accent variation, noise in real-world recordings, and multilingual audio processing.</li>\n</ul>\n<ul>\n<li>Work with technical staff to improve annotation tools for efficient audio workflows.</li>\n</ul>\n<p>Basic Qualifications:</p>\n<ul>\n<li>Native proficiency in Danish with exposure to diverse accents, dialects, or regional variations.</li>\n</ul>\n<ul>\n<li>Proficiency in English (minimum B2 level) with clear, natural vocal delivery and pronunciation suitable for audio recording purposes.</li>\n</ul>\n<ul>\n<li>Strong auditory perception to identify nuances in speech, accents, pronunciation, intonation, and audio quality across languages.</li>\n</ul>\n<ul>\n<li>Demonstrated ability to handle multilingual audio content, including evaluating speech accuracy, cultural vocal expressions, and contextual interpretation in spoken form.</li>\n</ul>\n<ul>\n<li>Demonstrated ability to transcribe audio with high accuracy across accents and varying audio quality.</li>\n</ul>\n<ul>\n<li>Comfort providing high-quality voice recordings and feedback on audio samples in multiple languages.</li>\n</ul>\n<ul>\n<li>Strong comprehension skills and the ability to make independent judgments on ambiguous or varied audio material, including noisy or accented speech.</li>\n</ul>\n<ul>\n<li>Strong communication, interpersonal, analytical, detail-oriented, and organizational skills, with the ability to articulate audio-related feedback effectively.</li>\n</ul>\n<ul>\n<li>Commitment to developing AI that masters sophisticated multilingual audio capabilities.</li>\n</ul>\n<p>Preferred Skills and Experience:</p>\n<ul>\n<li>Demonstration of exceptional attention to linguistic nuance, auditory detail, and data quality beyond standard transcription work.</li>\n</ul>\n<ul>\n<li>Deep understanding and taste of what good/useful Audio data is.</li>\n</ul>\n<ul>\n<li>Strong command of advanced transcription and annotation practices, including handling disfluencies, accents, and prosodic features (intonation, stress, rhythm, emotion, etc) with high consistency and accuracy.</li>\n</ul>\n<ul>\n<li>Background in linguistics (e.g., phonetics, phonology, sociolinguistics), speech sciences, cognitive science, or a related field, or equivalent practical experience, with demonstrated ability to analyze accent variation, pronunciation differences, and multilingual speech patterns.</li>\n</ul>\n<ul>\n<li>Experience working with speech/audio datasets, annotation workflows, or AI training data, including knowledge/experience with training voice models, and an understanding of how data quality impacts model performance.</li>\n</ul>\n<ul>\n<li>Professional experience in voice work, including voice acting, voice recording, podcasting with a measurable audience (e.g., X following), or similar audio production demonstrating attention to clarity and recording quality.</li>\n</ul>\n<ul>\n<li>Demonstrated ability to exercise independent judgment in ambiguous audio scenarios and make consistent, defensible annotation decisions.</li>\n</ul>\n<ul>\n<li>Portfolio (strongly preferred for advanced candidates): Voice samples, annotated transcripts, or audio-related work demonstrating quality, methodology, and attention to detail.</li>\n</ul>\n<ul>\n<li>Candidates with professional experience in voice, linguistics, speech data, or speech evaluation and research are especially encouraged to apply.</li>\n</ul>\n<p>Location and Other Expectations:</p>\n<ul>\n<li>Tutor roles may be offered as full-time, part-time, or contractor positions, depending on role needs and candidate fit.</li>\n</ul>\n<ul>\n<li>For contractor positions, hours will vary widely based on project scope and contractor availability, with no fixed commitments required. On average, most projects may require at least 10 hours per week to deliver effectively, though this is not a fixed commitment and depends on the scope of work. Contractors have full flexibility to set their own hours and determine the exact amount of time needed to complete deliverables.</li>\n</ul>\n<ul>\n<li>Tutor roles may be performed remotely from any location worldwide, subject to legal eligibility, time-zone compatibility, and role-specific needs.</li>\n</ul>\n<ul>\n<li>For US-based candidates, please note that we are unable to hire in Wyoming and Illinois at this time.</li>\n</ul>\n<ul>\n<li>We are unable to provide visa sponsorship.</li>\n</ul>\n<ul>\n<li>For those who will be working from a personal device, your computer must be a Chromebook, a Mac with macOS 11.0 or later, or Windows 10 or later.</li>\n</ul>\n<p>Compensation and Benefits:</p>\n<p>US-based candidates: $35/hour - $45/hour depending on factors including relevant experience, skills, education, geographic location, and qualifications. International candidates: Information will be provided to you during the recruitment process.</p>\n<p>Benefits vary based on employment type, location, and jurisdiction. Benefits for eligible U.S.-based positions include health insurance, 401(k) plan, and paid sick leave. Specific details and role-specific information will be provided to you during the interview process.</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_c85087bb-6a8","directApply":true,"hiringOrganization":{"@type":"Organization","name":"xAI","sameAs":"https://www.xai.com/","logo":"https://logos.yubhub.co/xai.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/xai/jobs/5090189007","x-work-arrangement":"remote","x-experience-level":"mid","x-job-type":"full-time|part-time|contract","x-salary-range":"$35/hour - $45/hour","x-skills-required":["Multilingual audio capabilities","Proprietary software","Audio data curation","Annotation tools","Speech recognition","Auditory experiences","Diverse languages","Accents","Cultural contexts","Linguistic and prosodic details","Professional audio standards","Speech modulation","Accent variation","Noise in real-world recordings","Multilingual audio processing","Audio workflows","Transcription","Audio quality","Voice recordings","Feedback on audio samples","Independent judgments","Ambiguous audio scenarios","Defensible annotation decisions","Portfolio","Voice samples","Annotated transcripts","Audio-related work","Quality","Methodology","Attention to detail","Professional experience in voice","Linguistics","Speech data","Speech evaluation and research"],"x-skills-preferred":["Exceptional attention to linguistic nuance","Auditory detail","Data quality","Advanced transcription and annotation practices","Disfluencies","Prosodic features","Intonation","Stress","Rhythm","Emotion","Phonetics","Phonology","Sociolinguistics","Speech sciences","Cognitive science","Pronunciation differences","Multilingual speech patterns","Speech/audio datasets","Annotation workflows","AI training data","Training voice models","Data quality impacts model performance","Voice acting","Voice recording","Podcasting","Measurable audience","Clarity and recording quality"],"datePosted":"2026-04-18T15:57:57.773Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Remote"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Multilingual audio capabilities, Proprietary software, Audio data curation, Annotation tools, Speech recognition, Auditory experiences, Diverse languages, Accents, Cultural contexts, Linguistic and prosodic details, Professional audio standards, Speech modulation, Accent variation, Noise in real-world recordings, Multilingual audio processing, Audio workflows, Transcription, Audio quality, Voice recordings, Feedback on audio samples, Independent judgments, Ambiguous audio scenarios, Defensible annotation decisions, Portfolio, Voice samples, Annotated transcripts, Audio-related work, Quality, Methodology, Attention to detail, Professional experience in voice, Linguistics, Speech data, Speech evaluation and research, Exceptional attention to linguistic nuance, Auditory detail, Data quality, Advanced transcription and annotation practices, Disfluencies, Prosodic features, Intonation, Stress, Rhythm, Emotion, Phonetics, Phonology, Sociolinguistics, Speech sciences, Cognitive science, Pronunciation differences, Multilingual speech patterns, Speech/audio datasets, Annotation workflows, AI training data, Training voice models, Data quality impacts model performance, Voice acting, Voice recording, Podcasting, Measurable audience, Clarity and recording quality"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_bd9625d9-99b"},"title":"ML Infrastructure Engineer, Safeguards","description":"<p>We are seeking a Machine Learning Infrastructure Engineer to join our Safeguards organization, where you&#39;ll build and scale the critical infrastructure that powers our AI safety systems.</p>\n<p>As part of the Safeguards team, you&#39;ll design and implement ML infrastructure that powers Claude safety. Your work will directly contribute to making AI systems more trustworthy and aligned with human values, ensuring our models operate safely as they become more capable.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Design and build scalable ML infrastructure to support real-time and batch classifier and safety evaluations across our model ecosystem</li>\n<li>Build monitoring and observability tools to track model performance, data quality, and system health for safety-critical applications</li>\n<li>Collaborate with research teams to productionize safety research, translating experimental safety techniques into robust, scalable systems</li>\n<li>Optimize inference latency and throughput for real-time safety evaluations while maintaining high reliability standards</li>\n<li>Implement automated testing, deployment, and rollback systems for ML models in production safety applications</li>\n<li>Partner with Safeguards, Security, and Alignment teams to understand requirements and deliver infrastructure that meets safety and production needs</li>\n<li>Contribute to the development of internal tools and frameworks that accelerate safety research and deployment</li>\n</ul>\n<p>You may be a good fit if you:</p>\n<ul>\n<li>Have 5+ years of experience building production ML infrastructure, ideally in safety-critical domains like fraud detection, content moderation, or risk assessment</li>\n<li>Are proficient in Python and have experience with ML frameworks like PyTorch, TensorFlow, or JAX</li>\n<li>Have hands-on experience with cloud platforms (AWS, GCP) and container orchestration (Kubernetes)</li>\n<li>Understand distributed systems principles and have built systems that handle high-throughput, low-latency workloads</li>\n<li>Have experience with data engineering tools and building robust data pipelines (e.g., Spark, Airflow, streaming systems)</li>\n<li>Are results-oriented, with a bias towards reliability and impact in safety-critical systems</li>\n<li>Enjoy collaborating with researchers and translating cutting-edge research into production systems</li>\n<li>Care deeply about AI safety and the societal impacts of your work</li>\n</ul>\n<p>Strong candidates may have experience with:</p>\n<ul>\n<li>Working with large language models and modern transformer architectures</li>\n<li>Implementing A/B testing frameworks and experimentation infrastructure for ML systems</li>\n<li>Developing monitoring and alerting systems for ML model performance and data drift</li>\n<li>Building automated labeling systems and human-in-the-loop workflows</li>\n<li>Experience in trust &amp; safety, fraud prevention, or content moderation domains</li>\n<li>Knowledge of privacy-preserving ML techniques and compliance requirements</li>\n<li>Contributing to open-source ML infrastructure projects</li>\n</ul>\n<p>We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_bd9625d9-99b","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://www.anthropic.com/","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/4778843008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$320,000-$405,000 USD","x-skills-required":["Python","PyTorch","TensorFlow","JAX","Cloud platforms (AWS, GCP)","Container orchestration (Kubernetes)","Distributed systems principles","Data engineering tools (Spark, Airflow, streaming systems)"],"x-skills-preferred":["Large language models and modern transformer architectures","A/B testing frameworks and experimentation infrastructure for ML systems","Monitoring and alerting systems for ML model performance and data drift","Automated labeling systems and human-in-the-loop workflows","Trust & safety, fraud prevention, or content moderation domains","Privacy-preserving ML techniques and compliance requirements","Open-source ML infrastructure projects"],"datePosted":"2026-04-18T15:44:06.907Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, PyTorch, TensorFlow, JAX, Cloud platforms (AWS, GCP), Container orchestration (Kubernetes), Distributed systems principles, Data engineering tools (Spark, Airflow, streaming systems), Large language models and modern transformer architectures, A/B testing frameworks and experimentation infrastructure for ML systems, Monitoring and alerting systems for ML model performance and data drift, Automated labeling systems and human-in-the-loop workflows, Trust & safety, fraud prevention, or content moderation domains, Privacy-preserving ML techniques and compliance requirements, Open-source ML infrastructure projects","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":320000,"maxValue":405000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_dc6154f8-cff"},"title":"Research Engineer, Pretraining Scaling - London","description":"<p>About Anthropic\\n\\nAnthropic&#39;s mission is to create reliable, interpretable, and steerable AI systems.\\n\\nAbout the Role:\\n\\nAs a Research Engineer on Anthropic&#39;s ML Performance and Scaling team, you&#39;ll ensure our frontier models train reliably, efficiently, and at scale. This is demanding, high-impact work that requires both deep technical expertise and a genuine passion for the craft of large-scale ML systems.\\n\\nResponsibilities:\\n\\n- Own critical aspects of our production pretraining pipeline, including model operations, performance optimization, observability, and reliability\\n- Debug and resolve complex issues across the full stack,from hardware errors and networking to training dynamics and evaluation infrastructure\\n- Design and run experiments to improve training efficiency, reduce step time, increase uptime, and enhance model performance\\n- Respond to on-call incidents during model launches, diagnosing problems quickly and coordinating solutions across teams\\n- Build and maintain production logging, monitoring dashboards, and evaluation infrastructure\\n- Add new capabilities to the training codebase, such as long context support or novel architectures\\n- Collaborate closely with teammates across SF and London, as well as with Tokens, Architectures, and Systems teams\\n- Contribute to the team&#39;s institutional knowledge by documenting systems, debugging approaches, and lessons learned\\n\\nYou May Be a Good Fit If You:\\n\\n- Have hands-on experience training large language models, or deep expertise with JAX, TPU, PyTorch, or large-scale distributed systems\\n- Genuinely enjoy both research and engineering work,you&#39;d describe your ideal split as roughly 50/50 rather than heavily weighted toward one or the other\\n- Are excited about being on-call for production systems, working long days during launches, and solving hard problems under pressure\\n- Thrive when working on whatever is most impactful, even if that changes day-to-day based on what the production model needs\\n- Excel at debugging complex, ambiguous problems across multiple layers of the stack\\n- Communicate clearly and collaborate effectively, especially when coordinating across time zones or during high-stress incidents\\n- Are passionate about the work itself and want to refine your craft as a research engineer\\n- Care about the societal impacts of AI and responsible scaling\\n\\nStrong Candidates May Also Have:\\n\\n- Previous experience training LLM’s or working extensively with JAX/TPU, PyTorch, or other ML frameworks at scale\\n- Contributed to open-source LLM frameworks (e.g., open_lm, llm-foundry, mesh-transformer-jax)\\n- Published research on model training, scaling laws, or ML systems\\n- Experience with production ML systems, observability tools, or evaluation infrastructure\\n- Background as a systems engineer, quant, or in other roles requiring both technical depth and operational excellence\\n\\nWhat Makes This Role Unique:\\n\\nThis is not a typical research engineering role. The work is highly operational,you&#39;ll be deeply involved in keeping our production models training smoothly, which means being responsive to incidents, flexible about priorities, and comfortable with uncertainty. During launches, the team often works extended hours and may need to respond to issues on evenings and weekends.\\n\\nHowever, this operational intensity comes with extraordinary learning opportunities. You&#39;ll gain hands-on experience with some of the largest, most sophisticated training runs in the industry. You&#39;ll work alongside world-class researchers and engineers, and the institutional knowledge you build will compound in ways that can&#39;t be easily transferred. For people who thrive on this type of work, it&#39;s uniquely rewarding.\\n\\nWe&#39;re building a close-knit team of people who genuinely care about doing excellent work together. If you&#39;re someone who wants to be part of training the models that will define the future of AI,and you&#39;re excited about the full reality of what that entails,we&#39;d love to hear from you.\\n\\nLocation:\\n\\nThis role requires working in-office 5 days per week in London.\\n\\nDeadline to apply:\\n\\nNone. Applications will be reviewed on a rolling basis.\\n\\nThe annual compensation range for this role is listed below.\\n\\nFor sales roles, the range provided is the role’s On Target Earnings (&quot;OTE&quot;) range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\\n\\nAnnual Salary:\\n\\n£260,000-£630,000 GBP\\n\\nLogistics\\n\\nMinimum education:\\n\\nBachelor’s degree or an equivalent combination of education, training, and/or experience\\n\\nRequired field of study:\\n\\nA field relevant to the role as demonstrated through coursework, training, or professional experience\\n\\nMinimum years of experience:\\n\\nYears of experience required will correlate with the internal job level requirements for the position\\n\\nLocation-based hybrid policy:\\n\\nCurrently, 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.\\n\\nVisa sponsorship:\\n\\nWe 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.\\n\\nWe 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. We think AI systems like the ones we&#39;re building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.\\n\\nYour 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.\\n\\nHow we&#39;re different\\n\\nWe 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 h</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_dc6154f8-cff","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/4938436008","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"£260,000-£630,000 GBP","x-skills-required":["JAX","TPU","PyTorch","large-scale distributed systems","model operations","performance optimization","observability","reliability","debugging","complex issues","hardware errors","networking","training dynamics","evaluation infrastructure","experiments","training efficiency","step time","uptime","model performance","production logging","monitoring dashboards","codebase","long context support","novel architectures","collaboration","institutional knowledge","documentation","debugging approaches","lessons learned"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:42:55.023Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"London, UK"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"JAX, TPU, PyTorch, large-scale distributed systems, model operations, performance optimization, observability, reliability, debugging, complex issues, hardware errors, networking, training dynamics, evaluation infrastructure, experiments, training efficiency, step time, uptime, model performance, production logging, monitoring dashboards, codebase, long context support, novel architectures, collaboration, institutional knowledge, documentation, debugging approaches, lessons learned","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_6960fd5f-0e8"},"title":"Research Engineer, Pretraining Scaling","description":"<p><strong>About the Role:\\n\\nAs a Research Engineer on Anthropic&#39;s ML Performance and Scaling team, you&#39;ll ensure our frontier models train reliably, efficiently, and at scale. This is demanding, high-impact work that requires both deep technical expertise and a genuine passion for the craft of large-scale ML systems.\\n\\n## Responsibilities:\\n\\n- Own critical aspects of our production pretraining pipeline, including model operations, performance optimization, observability, and reliability\\n- Debug and resolve complex issues across the full stack,from hardware errors and networking to training dynamics and evaluation infrastructure\\n- Design and run experiments to improve training efficiency, reduce step time, increase uptime, and enhance model performance\\n- Respond to on-call incidents during model launches, diagnosing problems quickly and coordinating solutions across teams\\n- Build and maintain production logging, monitoring dashboards, and evaluation infrastructure\\n- Add new capabilities to the training codebase, such as long context support or novel architectures\\n- Collaborate closely with teammates across SF and London, as well as with Tokens, Architectures, and Systems teams\\n- Contribute to the team&#39;s institutional knowledge by documenting systems, debugging approaches, and lessons learned\\n\\n## You May Be a Good Fit If You:\\n\\n- Have hands-on experience training large language models, or deep expertise with JAX, TPU, PyTorch, or large-scale distributed systems\\n- Genuinely enjoy both research and engineering work,you&#39;d describe your ideal split as roughly 50/50 rather than heavily weighted toward one or the other\\n- Are excited about being on-call for production systems, working long days during launches, and solving hard problems under pressure\\n- Thrive when working on whatever is most impactful, even if that changes day-to-day based on what the production model needs\\n- Excel at debugging complex, ambiguous problems across multiple layers of the stack\\n- Communicate clearly and collaborate effectively, especially when coordinating across time zones or during high-stress incidents\\n- Are passionate about the work itself and want to refine your craft as a research engineer\\n- Care about the societal impacts of AI and responsible scaling\\n\\n## Strong Candidates May Also Have:\\n\\n- Previous experience training LLM’s or working extensively with JAX/TPU, PyTorch, or other ML frameworks at scale\\n- Contributed to open-source LLM frameworks (e.g., open_lm, llm-foundry, mesh-transformer-jax)\\n- Published research on model training, scaling laws, or ML systems\\n- Experience with production ML systems, observability tools, or evaluation infrastructure\\n- Background as a systems engineer, quant, or in other roles requiring both technical depth and operational excellence\\n\\n## What Makes This Role Unique:\\n\\nThis is not a typical research engineering role. The work is highly operational,you&#39;ll be deeply involved in keeping our production models training smoothly, which means being responsive to incidents, flexible about priorities, and comfortable with uncertainty. During launches, the team often works extended hours and may need to respond to issues on evenings and weekends.\\n\\nHowever, this operational intensity comes with extraordinary learning opportunities. You&#39;ll gain hands-on experience with some of the largest, most sophisticated training runs in the industry. You&#39;ll work alongside world-class researchers and engineers, and the institutional knowledge you build will compound in ways that can&#39;t be easily transferred. For people who thrive on this type of work, it&#39;s uniquely rewarding.\\n\\nWe&#39;re building a close-knit team of people who genuinely care about doing excellent work together. If you&#39;re someone who wants to be part of training the models that will define the future of AI,and you&#39;re excited about the full reality of what that entails,we&#39;d love to hear from you.\\n\\nLocation: This role requires working in-office 5 days per week in San Francisco.\\n\\nDeadline to apply: None. Applications will be reviewed on a rolling basis.\\n\\nThe annual compensation range for this role is listed below.\\n\\nFor sales roles, the range provided is the role’s On Target Earnings (&quot;OTE&quot;) range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\\n\\nAnnual Salary: $350,000-$850,000 USD\\n\\n## Logistics\\n\\nMinimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\\n\\nRequired field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience\\n\\nMinimum years of experience: Years of experience required will correlate with the internal job level requirements for the position\\n\\nLocation-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.\\n\\nVisa 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.\\n\\nWe 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. We think AI systems like the ones we&#39;re building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.\\n\\nYour 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.\\n\\n## How we&#39;re different\\n\\nWe 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</strong></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_6960fd5f-0e8","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/4938432008","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$350,000-$850,000 USD","x-skills-required":["JAX","TPU","PyTorch","large-scale distributed systems","model operations","performance optimization","observability","reliability","debugging","complex issues","hardware errors","networking","training dynamics","evaluation infrastructure","experiments","training efficiency","step time","uptime","model performance","production logging","monitoring dashboards","new capabilities","long context support","novel architectures","collaboration","institutional knowledge","documentation","debugging approaches","lessons learned"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:42:31.268Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"JAX, TPU, PyTorch, large-scale distributed systems, model operations, performance optimization, observability, reliability, debugging, complex issues, hardware errors, networking, training dynamics, evaluation infrastructure, experiments, training efficiency, step time, uptime, model performance, production logging, monitoring dashboards, new capabilities, long context support, novel architectures, collaboration, institutional knowledge, documentation, debugging approaches, lessons learned","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":350000,"maxValue":850000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_3bf82bef-273"},"title":"Chemical Engineering Tutor","description":"<p>As an AI Tutor - Chemical Engineering specialist, you will enhance the capabilities of our cutting-edge technologies by providing high-quality input and labels using specialized software. 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Strong communication, interpersonal, analytical, and organisational capabilities are essential.</p>\n<p>Tutor roles may be offered as full-time, part-time, or contractor positions, depending on role needs and candidate fit. 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On average, most projects may require at least 10 hours per week to deliver effectively, though this is not a fixed commitment and depends on the scope of work. 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In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.</p>\n<ul>\n<li>Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts</li>\n</ul>\n<ul>\n<li>Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)</li>\n</ul>\n<ul>\n<li>401(k) retirement plan with employer match</li>\n</ul>\n<ul>\n<li>Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)</li>\n</ul>\n<ul>\n<li>Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees</li>\n</ul>\n<ul>\n<li>13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)</li>\n</ul>\n<ul>\n<li>Mental health and wellness support</li>\n</ul>\n<ul>\n<li>Employer-paid basic life and disability coverage</li>\n</ul>\n<ul>\n<li>Annual learning and development stipend to fuel your professional growth</li>\n</ul>\n<ul>\n<li>Daily meals in our offices, and meal delivery credits as eligible</li>\n</ul>\n<ul>\n<li>Relocation support for eligible employees</li>\n</ul>\n<ul>\n<li>Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.</li>\n</ul>\n<p>More details about our benefits are available to candidates during the hiring process.</p>\n<p>This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.</p>\n<p><strong>About the Team</strong></p>\n<p>OpenAI&#39;s Training team is responsible for producing the large language models that power our research, our products, and ultimately bring us closer to AGI. Achieving this goal requires combining deep research into improving our current architecture, datasets and optimization techniques, alongside long-term bets aimed at improving the efficiency and capability of future generations of models. We are responsible for integrating these techniques and producing model artifacts used by the rest of the company, and ensuring that these models are world-class in every respect. Recent examples of artifacts with major contributions from our team include GPT4-Turbo, GPT-4o and o1-mini.</p>\n<p><strong>About the Role</strong></p>\n<p>As a member of the architecture team, you will push the frontier of architecture development for OpenAI&#39;s flagship models, enhancing intelligence, efficiency, and adding new capabilities.</p>\n<p>Ideal candidates have a deep understanding of LLM architectures, a sophisticated understanding of model inference, and a hands-on empirical approach. A good fit for this role will be equally happy coming up with a creative breakthrough, investing in strengthening a baseline, designing an eval, debugging a thorny regression, or tracking down a bottleneck.</p>\n<p>This role is based in San Francisco. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.</p>\n<p><strong>In this role, you will:</strong></p>\n<ul>\n<li>Design, prototype and scale up new architectures to improve model intelligence</li>\n<li>Execute and analyze experiments autonomously and collaboratively</li>\n<li>Study, debug, and optimize both model performance and computational performance</li>\n<li>Contribute to training and inference infrastructure</li>\n</ul>\n<p><strong>You might thrive in this role if you:</strong></p>\n<ul>\n<li>Have experience landing contributions to major LLM training runs</li>\n<li>Can thoroughly evaluate and improve deep learning architectures in a self-directed fashion</li>\n<li>Are motivated by safely deploying LLMs in the real world</li>\n<li>Are well-versed in the state of the art transformer modifications for efficiency</li>\n</ul>\n<p><strong>About OpenAI</strong></p>\n<p>OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. 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