{"version":"0.1","company":{"name":"YubHub","url":"https://yubhub.co","jobsUrl":"https://yubhub.co/jobs/skill/cnns"},"x-facet":{"type":"skill","slug":"cnns","display":"Cnns","count":4},"x-feed-size-limit":100,"x-feed-sort":"enriched_at desc","x-feed-notice":"This feed contains at most 100 jobs (the most recently enriched). For the full corpus, use the paginated /stats/by-facet endpoint or /search.","x-generator":"yubhub-xml-generator","x-rights":"Free to redistribute with attribution: \"Data by YubHub (https://yubhub.co)\"","x-schema":"Each entry in `jobs` follows https://schema.org/JobPosting. YubHub-native raw fields carry `x-` prefix.","jobs":[{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_467be5c4-940"},"title":"Machine Learning Engineer","description":"<p>We&#39;re looking for a Machine Learning Engineer to join our Ads Engineering team. As a Machine Learning Engineer at Reddit, you will design and build production ML systems that power core experiences across the platform, including personalized recommendations, search, and ranking systems, intelligent advertising systems, and large-scale machine learning pipelines.</p>\n<p>Our team works on high-impact systems that operate at internet scale and directly influence user experience, advertiser value, and business outcomes. You&#39;ll work on complex, real-world ML problems at massive scale, and contribute to technical strategy, architecture, and long-term ML roadmap.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Design, build, and deploy production-grade machine learning models and systems at scale</li>\n<li>Own the full ML lifecycle: from problem definition and feature engineering to training, evaluation, deployment, and monitoring</li>\n<li>Build scalable data and model pipelines with strong reliability, observability, and automated retraining</li>\n<li>Work with large-scale datasets to improve ranking, recommendations, search relevance, prediction, content/user understanding, and optimization systems</li>\n<li>Partner cross-functionally with Product, Data Science, Infrastructure, and Engineering teams to translate complex problems into ML solutions</li>\n<li>Improve system performance across latency, throughput, and model quality metrics</li>\n<li>Research and apply state-of-the-art machine learning and AI techniques, including deep learning, graph &amp; transformers based, and LLM evaluation/alignment</li>\n</ul>\n<p>Basic Qualifications:</p>\n<ul>\n<li>3-5+ years of experience building, deploying, and operating machine learning systems in production</li>\n<li>Strong programming skills in Python, Java, Go, or similar languages, with solid software engineering fundamentals</li>\n<li>ML Fundamentals: a strong grasp of algorithms, from classic statistical learning (XGBoost, Random Forests, regressions) to DL architectures (Transformers, CNNs, GNNs)</li>\n<li>Hands-on experience with modern ML frameworks (e.g., PyTorch, TensorFlow)</li>\n<li>Experience designing scalable ML pipelines, data processing systems, and model serving infrastructure</li>\n<li>Ability to work cross-functionally and translate ambiguous product or business problems into technical solutions</li>\n<li>Experience improving measurable metrics through applied machine learning</li>\n</ul>\n<p>Preferred Qualifications:</p>\n<ul>\n<li>Experience with recommender systems, search/ranking systems, advertising/auction systems, large-scale representation learning, or multimodal embedding systems</li>\n<li>Familiarity with distributed systems and large-scale data processing (Spark, Kafka, Ray, Airflow, BigQuery, Redis, etc.)</li>\n<li>Experience working with real-time systems and low-latency production environments</li>\n<li>Background in feature engineering, model optimization, and production monitoring</li>\n<li>Experience with LLM/Gen AI techniques, including but not limited to LLM evaluation, alignment, fine-tuning, knowledge distillation, RAG/agentic systems and productionizing LLM-powered products at scale</li>\n<li>Advanced degree in Computer Science, Machine Learning, or related quantitative field</li>\n</ul>\n<p>Potential Teams:</p>\n<ul>\n<li>Ads Measurement Modeling</li>\n<li>Ads Targeting and Retrieval</li>\n<li>Advertiser Optimization</li>\n<li>Ads Marketplace Quality</li>\n<li>Ads Creative Effectiveness</li>\n<li>Ads Foundational Representations</li>\n<li>Ads Content Understanding</li>\n<li>Ads Ranking</li>\n<li>Feed Relevance</li>\n<li>Search and Answers Relevance</li>\n<li>ML Understanding</li>\n<li>Notifications Relevance</li>\n</ul>\n<p>Benefits:</p>\n<ul>\n<li>Comprehensive Healthcare Benefits and Income Replacement Programs</li>\n<li>401k with Employer Match</li>\n<li>Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support</li>\n<li>Family Planning Support</li>\n<li>Gender-Affirming Care</li>\n<li>Mental Health &amp; Coaching Benefits</li>\n<li>Flexible Vacation &amp; Paid Volunteer Time Off</li>\n<li>Generous Paid Parental Leave</li>\n</ul>\n<p>Pay Transparency:</p>\n<p>This job posting may span more than one career level. In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave.</p>\n<p>To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below.</p>\n<p>The base salary range for this position is: $185,800-$260,100 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_467be5c4-940","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Reddit","sameAs":"https://www.redditinc.com","logo":"https://logos.yubhub.co/redditinc.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/reddit/jobs/7131932","x-work-arrangement":"remote","x-experience-level":"mid","x-job-type":"full-time","x-salary-range":"$185,800-$260,100 USD","x-skills-required":["Python","Java","Go","PyTorch","TensorFlow","XGBoost","Random Forests","Regressions","Transformers","CNNs","GNNs","Spark","Kafka","Ray","Airflow","BigQuery","Redis"],"x-skills-preferred":["Recommender systems","Search/ranking systems","Advertising/auction systems","Large-scale representation learning","Multimodal embedding systems","Distributed systems","Large-scale data processing","Real-time systems","Low-latency production environments","Feature engineering","Model optimization","Production monitoring","LLM/Gen AI techniques"],"datePosted":"2026-04-18T15:57:49.850Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Remote - United States"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, Java, Go, PyTorch, TensorFlow, XGBoost, Random Forests, Regressions, Transformers, CNNs, GNNs, Spark, Kafka, Ray, Airflow, BigQuery, Redis, Recommender systems, Search/ranking systems, Advertising/auction systems, Large-scale representation learning, Multimodal embedding systems, Distributed systems, Large-scale data processing, Real-time systems, Low-latency production environments, Feature engineering, Model optimization, Production monitoring, LLM/Gen AI techniques","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":185800,"maxValue":260100,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_e998910e-d8f"},"title":"Senior Machine Learning Engineer","description":"<p>We&#39;re looking for a Senior Machine Learning Engineer to join our Ads Engineering team. As a Senior Machine Learning Engineer at Reddit, you will design and build production ML systems that power core experiences across the platform, including personalized recommendations, search, and ranking systems. You&#39;ll work on high-impact systems that operate at internet scale and directly influence user experience, advertiser value, and business outcomes.</p>\n<p>Your responsibilities will include:</p>\n<ul>\n<li>Designing, building, and deploying production-grade machine learning models and systems at scale</li>\n<li>Owning the full ML lifecycle: from problem definition and feature engineering to training, evaluation, deployment, and monitoring</li>\n<li>Building scalable data and model pipelines with strong reliability, observability, and automated retraining</li>\n<li>Working with large-scale datasets to improve ranking, recommendations, search relevance, prediction, content/user understanding, and optimization systems</li>\n<li>Partnering cross-functionally with Product, Data Science, Infrastructure, and Engineering teams to translate complex problems into ML solutions</li>\n</ul>\n<p>You&#39;ll work on a wide range of high-impact problems across the Reddit ecosystem, including recommender systems, search/ranking systems, advertising/auction systems, large-scale representation learning, and multimodal embedding systems.</p>\n<p>To be successful in this role, you&#39;ll need:</p>\n<ul>\n<li>3-5+ years of experience building, deploying, and operating machine learning systems in production</li>\n<li>Strong programming skills in Python, Java, Go, or similar languages, with solid software engineering fundamentals</li>\n<li>ML Fundamentals: a strong grasp of algorithms, from classic statistical learning (XGBoost, Random Forests, regressions) to DL architectures (Transformers, CNNs, GNNs)</li>\n<li>Hands-on experience with modern ML frameworks (e.g., PyTorch, TensorFlow)</li>\n<li>Experience designing scalable ML pipelines, data processing systems, and model serving infrastructure</li>\n<li>Ability to work cross-functionally and translate ambiguous product or business problems into technical solutions</li>\n</ul>\n<p>Preferred qualifications include experience with recommender systems, search/ranking systems, advertising/auction systems, large-scale representation learning, or multimodal embedding systems, familiarity with distributed systems and large-scale data processing, and experience working with real-time systems and low-latency production environments.</p>\n<p>At Reddit, we&#39;re committed to building a workforce representative of the diverse communities we serve. We&#39;re proud to be an equal opportunity employer and are committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures.</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_e998910e-d8f","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Reddit","sameAs":"https://www.redditinc.com","logo":"https://logos.yubhub.co/redditinc.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/reddit/jobs/6960833","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["Python","Java","Go","PyTorch","TensorFlow","XGBoost","Random Forests","Regressions","Transformers","CNNs","GNNs"],"x-skills-preferred":["Recommender systems","Search/ranking systems","Advertising/auction systems","Large-scale representation learning","Multimodal embedding systems","Distributed systems","Large-scale data processing","Real-time systems","Low-latency production environments"],"datePosted":"2026-04-18T15:56:54.058Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Remote - Ontario, Canada"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, Java, Go, PyTorch, TensorFlow, XGBoost, Random Forests, Regressions, Transformers, CNNs, GNNs, Recommender systems, Search/ranking systems, Advertising/auction systems, Large-scale representation learning, Multimodal embedding systems, Distributed systems, Large-scale data processing, Real-time systems, Low-latency production environments"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_fc38e24f-97e"},"title":"Senior Machine Learning Engineer","description":"<p>We&#39;re looking for a Senior Machine Learning Engineer to join our Ads Engineering team. As a key member of our team, you will design and build production ML systems that power core experiences across the platform, including personalized recommendations, search, and ranking systems, intelligent advertising systems, and large-scale machine learning pipelines.</p>\n<p>Our team is responsible for building systems that operate at internet scale and directly influence user experience, advertiser value, and business outcomes. You will work on high-impact systems that improve ranking, recommendations, search relevance, prediction, content/user understanding, and optimization systems.</p>\n<p>As a Senior Machine Learning Engineer, you will:</p>\n<ul>\n<li>Design, build, and deploy production-grade machine learning models and systems at scale</li>\n<li>Own the full ML lifecycle: from problem definition and feature engineering to training, evaluation, deployment, and monitoring</li>\n<li>Build scalable data and model pipelines with strong reliability, observability, and automated retraining</li>\n<li>Work with large-scale datasets to improve ranking, recommendations, search relevance, prediction, content/user understanding, and optimization systems</li>\n<li>Partner cross-functionally with Product, Data Science, Infrastructure, and Engineering teams to translate complex problems into ML solutions</li>\n<li>Improve system performance across latency, throughput, and model quality metrics</li>\n<li>Research and apply state-of-the-art machine learning and AI techniques, including deep learning, graph &amp; transformers based, and LLM evaluation/alignment</li>\n</ul>\n<p>Basic Qualifications:</p>\n<ul>\n<li>3-5+ years of experience building, deploying, and operating machine learning systems in production</li>\n<li>Strong programming skills in Python, Java, Go, or similar languages, with solid software engineering fundamentals</li>\n<li>ML Fundamentals: a strong grasp of algorithms, from classic statistical learning (XGBoost, Random Forests, regressions) to DL architectures (Transformers, CNNs, GNNs)</li>\n<li>Hands-on experience with modern ML frameworks (e.g., PyTorch, TensorFlow)</li>\n<li>Experience designing scalable ML pipelines, data processing systems, and model serving infrastructure</li>\n<li>Ability to work cross-functionally and translate ambiguous product or business problems into technical solutions</li>\n<li>Experience improving measurable metrics through applied machine learning</li>\n</ul>\n<p>Preferred Qualifications:</p>\n<ul>\n<li>Experience with recommender systems, search/ranking systems, advertising/auction systems, large-scale representation learning, or multimodal embedding systems</li>\n<li>Familiarity with distributed systems and large-scale data processing (Spark, Kafka, Ray, Airflow, BigQuery, Redis, etc.)</li>\n<li>Experience working with real-time systems and low-latency production environments</li>\n<li>Background in feature engineering, model optimization, and production monitoring</li>\n<li>Experience with LLM/Gen AI techniques, including but not limited to LLM evaluation, alignment, fine-tuning, knowledge distillation, RAG/agentic systems and productionizing LLM-powered products at scale</li>\n<li>Advanced degree in Computer Science, Machine Learning, or related quantitative field</li>\n</ul>\n<p>Potential Teams:</p>\n<ul>\n<li>Ads Measurement Modeling</li>\n<li>Ads Targeting and Retrieval</li>\n<li>Advertiser Optimization</li>\n<li>Ads Marketplace Quality</li>\n<li>Ads Creative Effectiveness</li>\n<li>Ads Foundational Representations</li>\n<li>Ads Content Understanding</li>\n<li>Ads Ranking</li>\n<li>Feed Relevance</li>\n<li>Search and Answers Relevance</li>\n<li>ML Understanding</li>\n<li>Notifications Relevance</li>\n</ul>\n<p>Benefits:</p>\n<ul>\n<li>Comprehensive Healthcare Benefits and Income Replacement Programs</li>\n<li>401k with Employer Match</li>\n<li>Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support</li>\n<li>Family Planning Support</li>\n<li>Gender-Affirming Care</li>\n<li>Mental Health &amp; Coaching Benefits</li>\n<li>Flexible Vacation &amp; Paid Volunteer Time Off</li>\n<li>Generous Paid Parental Leave</li>\n</ul>\n<p>Pay Transparency:</p>\n<p>This job posting may span more than one career level. In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit https://www.redditinc.com/careers/. To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below. The base salary range for this position is $216,700-$303,400 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_fc38e24f-97e","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Reddit","sameAs":"https://www.redditinc.com","logo":"https://logos.yubhub.co/redditinc.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/reddit/jobs/6960831","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$216,700-$303,400 USD","x-skills-required":["Python","Java","Go","PyTorch","TensorFlow","XGBoost","Random Forests","Regressions","Transformers","CNNs","GNNs","Spark","Kafka","Ray","Airflow","BigQuery","Redis"],"x-skills-preferred":["Recommender systems","Search/ranking systems","Advertising/auction systems","Large-scale representation learning","Multimodal embedding systems","Distributed systems","Large-scale data processing","Real-time systems","Low-latency production environments","Feature engineering","Model optimization","Production monitoring","LLM/Gen AI techniques","LLM evaluation","Alignment","Fine-tuning","Knowledge distillation","RAG/agentic systems"],"datePosted":"2026-04-18T15:45:58.533Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Remote - United States"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, Java, Go, PyTorch, TensorFlow, XGBoost, Random Forests, Regressions, Transformers, CNNs, GNNs, Spark, Kafka, Ray, Airflow, BigQuery, Redis, Recommender systems, Search/ranking systems, Advertising/auction systems, Large-scale representation learning, Multimodal embedding systems, Distributed systems, Large-scale data processing, Real-time systems, Low-latency production environments, Feature engineering, Model optimization, Production monitoring, LLM/Gen AI techniques, LLM evaluation, Alignment, Fine-tuning, Knowledge distillation, RAG/agentic systems","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":216700,"maxValue":303400,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_4e51470c-8f1"},"title":"Software Engineer, Accelerators","description":"<p><strong>Software Engineer, Accelerators</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>Department</strong></p>\n<p>Scaling</p>\n<p><strong>Compensation</strong></p>\n<ul>\n<li>$295K – $380K • 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. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. 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<p><strong>Benefits</strong></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><strong>About the Team</strong></p>\n<p>The Kernels team at OpenAI builds the low-level software that accelerates our most ambitious AI research.</p>\n<p>We work at the boundary of hardware and software, developing high-performance kernels, distributed system optimizations, and runtime improvements to make large-scale training and inference more efficient.</p>\n<p>Our work enables OpenAI to push the limits by ensuring models - from LLMs to recommender systems - to run reliably on advanced supercomputing platforms. That includes adapting our software stack to new types of accelerators, tuning system performance end-to-end, and removing bottlenecks across every layer of the stack.</p>\n<p><strong>About the Role</strong></p>\n<p>On the Accelerators team, you will help OpenAI evaluate and bring up new compute platforms that can support large-scale AI training and inference.</p>\n<p>Your work will range from prototyping system software on new accelerators to enabling performance optimizations across our AI workloads.</p>\n<p>You’ll work across the stack, collaborating with both hardware and software aspects - working on kernels, sharding strategies, scaling across distributed systems, and performance modeling.</p>\n<p>You&#39;ll help adapt OpenAI&#39;s software stack to non-traditional hardware and drive efficiency improvements in core AI workloads. This is not a compiler-focused role, rather bridging ML algorithms with system performance - especially at scale.</p>\n<p><strong>In this role, you will:</strong></p>\n<ul>\n<li>Prototype and enable OpenAI&#39;s AI software stack on new, exploratory accelerator platforms.</li>\n</ul>\n<ul>\n<li>Optimize large-scale model performance (LLMs, recommender systems, distributed AI workloads) for diverse hardware environments.</li>\n</ul>\n<ul>\n<li>Develop kernels, sharding mechanisms, and system scaling strategies tailored to emerging accelerators.</li>\n</ul>\n<ul>\n<li>Collaborate on optimizations at the model code level (e.g. PyTorch) and below to enhance performance on non-traditional hardware.</li>\n</ul>\n<p>Perform system-level performance modeling, debug bottlenecks, and drive end-to-end optimization.</p>\n<ul>\n<li>Work with hardware teams and vendors to evaluate alternatives to existing platforms and adapt the software stack to their architectures.</li>\n</ul>\n<ul>\n<li>Contribute to runtime improvements, compute/communication overlapping, and scaling efforts for frontier AI workloads.</li>\n</ul>\n<p><strong>You might thrive in this role if you have:</strong></p>\n<ul>\n<li>3+ years of experience working on AI infrastructure, including kernels, systems, or hardware-software co-design</li>\n</ul>\n<ul>\n<li>Hands-on experience with accelerator platforms for AI at data center scale (e.g., TPUs, custom silicon, exploratory architectures).</li>\n</ul>\n<ul>\n<li>Strong understanding of kernels, sharding, runtime systems, or distributed scaling techniques.</li>\n</ul>\n<ul>\n<li>Familiarity with optimizing LLMs, CNNs, or recommender models for hardware efficiency.</li>\n</ul>\n<ul>\n<li>Experience with performance modeling, system debugging, and software stack adaptation for novel architectures.</li>\n</ul>\n<ul>\n<li>Exposure to mobile accelerators is welcome, but experience enabling data center-scale AI hardware is preferred.</li>\n</ul>\n<ul>\n<li>Ability to operate across multiple levels of the stack, rapidly prototype solutions, and navigate ambiguity in early hardware bring-up phases</li>\n</ul>\n<ul>\n<li>Interest in shaping the future of AI compute through exploration of alternatives to mainstream accelerators.</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_4e51470c-8f1","directApply":true,"hiringOrganization":{"@type":"Organization","name":"OpenAI","sameAs":"https://jobs.ashbyhq.com","logo":"https://logos.yubhub.co/openai.com.png"},"x-apply-url":"https://jobs.ashbyhq.com/openai/f386b209-1259-4b79-bf5a-aa97fc7ce77b","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$295K – $380K • Offers Equity","x-skills-required":["AI infrastructure","kernels","systems","hardware-software co-design","accelerator platforms","TPUs","custom silicon","exploratory architectures","kernels","sharding","runtime systems","distributed scaling techniques","LLMs","CNNs","recommender models","hardware efficiency","performance modeling","system debugging","software stack adaptation","novel architectures"],"x-skills-preferred":["mobile accelerators","data center-scale AI hardware"],"datePosted":"2026-03-06T18:27:12.141Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"AI infrastructure, kernels, systems, hardware-software co-design, accelerator platforms, TPUs, custom silicon, exploratory architectures, kernels, sharding, runtime systems, distributed scaling techniques, LLMs, CNNs, recommender models, hardware efficiency, performance modeling, system debugging, software stack adaptation, novel architectures, mobile accelerators, data center-scale AI hardware","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":295000,"maxValue":380000,"unitText":"YEAR"}}}]}