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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_cba88898-896"},"title":"Research Engineer, Infrastructure, Kernels","description":"<p>We&#39;re looking for an infrastructure research engineer to design, optimize, and maintain the compute foundations that power large-scale language model training. You will develop high-performance ML kernels (e.g., CUDA, CuTe, Triton), enable efficient low-precision arithmetic, and improve the distributed compute stack that makes training large models possible.</p>\n<p>This role is perfect for an engineer who enjoys working close to the metal and across the research boundary. You&#39;ll collaborate with researchers and systems architects to bridge algorithmic design with hardware efficiency. 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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GenAI inference","description":"<p>As a software engineer for GenAI inference, you will help design, develop, and optimize the inference engine that powers Databricks&#39; Foundation Model API. You&#39;ll work at the intersection of research and production, ensuring our large language model (LLM) serving systems are fast, scalable, and efficient.</p>\n<p>Your work will touch the full GenAI inference stack , from kernels and runtimes to orchestration and memory management. You will contribute to the design and implementation of the inference engine, and collaborate on model-serving stack optimized for large-scale LLMs inference.</p>\n<p>Key responsibilities include:</p>\n<ul>\n<li>Collaborating with researchers to bring new model architectures or features (sparsity, activation compression, mixture-of-experts) into the engine</li>\n<li>Optimizing for latency, throughput, memory efficiency, and hardware utilization across GPUs, and accelerators</li>\n<li>Building and maintaining instrumentation, profiling, and tracing tooling to uncover bottlenecks and guide optimizations</li>\n<li>Developing and enhancing scalable routing, batching, scheduling, memory management, and dynamic loading mechanisms for inference workloads</li>\n<li>Supporting reliability, reproducibility, and fault tolerance in the inference pipelines, including A/B launches, rollback, and model versioning</li>\n<li>Integrating with federated, distributed inference infrastructure – orchestrate 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networking, scheduler)</li>\n<li>Experience building instrumentation, tracing, and profiling tools for ML models</li>\n<li>Ability to work closely with ML researchers, translate novel model ideas into production systems</li>\n<li>Ownership mindset and eagerness to dive deep into complex system challenges</li>\n<li>Bonus: published research or open-source contributions in ML systems, inference optimization, or model serving</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_f2196e99-854","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Databricks","sameAs":"https://databricks.com","logo":"https://logos.yubhub.co/databricks.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/databricks/jobs/8202670002","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$142,200-$204,600 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change</li>\n</ul>\n<p><strong>Nice to have:</strong></p>\n<ul>\n<li>High performance, large-scale ML systems</li>\n<li>GPU/Accelerator programming</li>\n<li>ML framework internals</li>\n<li>OS internals</li>\n<li>Language modeling with transformers</li>\n</ul>\n<p><strong>Compensation:</strong></p>\n<p>The annual compensation range for this role is $425,000-$560,000 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_8eec7f08-8c5","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/4741102008","x-work-arrangement":"hybrid","x-experience-level":"mid","x-job-type":"full-time","x-salary-range":"$425,000-$560,000 USD","x-skills-required":["Machine Learning","AI","Performance 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GPU Performance Engineer at Anthropic, you will be responsible for architecting and implementing the foundational systems that power Claude and push the frontiers of what&#39;s possible with large language models. You will maximize GPU utilization and performance at unprecedented scale, develop cutting-edge optimizations that directly enable new model capabilities, and dramatically improve inference efficiency.</p>\n<p>Working at the intersection of hardware and software, you will implement state-of-the-art techniques from custom kernel development to distributed system architectures. Your work will span the entire stack,from low-level tensor core optimizations to orchestrating thousands of GPUs in perfect synchronization.</p>\n<p>Strong candidates will have a track record of delivering transformative GPU performance improvements in production ML systems and will be excited to shape the future of AI infrastructure alongside world-class researchers and engineers.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Architect and implement foundational systems that power Claude</li>\n<li>Maximize GPU utilization and performance at unprecedented scale</li>\n<li>Develop cutting-edge optimizations that directly enable new model capabilities</li>\n<li>Dramatically improve inference efficiency</li>\n<li>Implement state-of-the-art techniques from custom kernel development to distributed system architectures</li>\n<li>Work at the intersection of hardware and software</li>\n<li>Span the entire stack,from low-level tensor core optimizations to orchestrating thousands of GPUs in perfect synchronization</li>\n</ul>\n<p>Requirements:</p>\n<ul>\n<li>Deep experience with GPU programming and optimization at scale</li>\n<li>Impact-driven, passionate about delivering measurable performance breakthroughs</li>\n<li>Ability to navigate complex systems from hardware interfaces to high-level ML frameworks</li>\n<li>Enjoy collaborative problem-solving and pair programming</li>\n<li>Want to work on state-of-the-art language models with real-world impact</li>\n<li>Care about the societal impacts of your work</li>\n<li>Thrive in ambiguous environments where you define the path forward</li>\n</ul>\n<p>Nice to have:</p>\n<ul>\n<li>Experience with GPU Kernel Development: CUDA, Triton, CUTLASS, Flash Attention, tensor core optimization</li>\n<li>ML Compilers &amp; Frameworks: PyTorch/JAX internals, torch.compile, XLA, custom operators</li>\n<li>Performance Engineering: Kernel fusion, memory bandwidth optimization, profiling with Nsight</li>\n<li>Distributed Systems: NCCL, NVLink, collective communication, model parallelism</li>\n<li>Low-Precision: INT8/FP8 quantization, mixed-precision techniques</li>\n<li>Production Systems: Large-scale training infrastructure, fault tolerance, cluster orchestration</li>\n</ul>\n<p>Representative projects:</p>\n<ul>\n<li>Co-design attention mechanisms and algorithms for next-generation hardware architectures</li>\n<li>Develop custom kernels for emerging quantization formats and mixed-precision techniques</li>\n<li>Design distributed communication strategies for multi-node GPU clusters</li>\n<li>Optimize end-to-end training and inference pipelines for frontier language models</li>\n<li>Build performance modeling frameworks to predict and optimize GPU utilization</li>\n<li>Implement kernel fusion strategies to minimize memory bandwidth bottlenecks</li>\n<li>Create resilient systems for planet-scale distributed training infrastructure</li>\n<li>Profile and eliminate performance bottlenecks in production serving infrastructure</li>\n<li>Partner with hardware vendors to influence future accelerator capabilities and software stacks</li>\n</ul>\n<p>Note: The salary range for this position is $280,000-$850,000 USD per year.</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_28107212-128","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/4926227008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$280,000-$850,000 USD per year","x-skills-required":["GPU programming","optimization at scale","CUDA","Triton","CUTLASS","Flash Attention","tensor core optimization","PyTorch/JAX internals","torch.compile","XLA","custom operators","kernel fusion","memory bandwidth optimization","profiling with Nsight","NCCL","NVLink","collective communication","model parallelism","INT8/FP8 quantization","mixed-precision techniques","large-scale training infrastructure","fault tolerance","cluster orchestration"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:40:11.758Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA | New York City, NY | Seattle, WA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"GPU programming, optimization at scale, CUDA, Triton, CUTLASS, Flash Attention, tensor core optimization, PyTorch/JAX internals, torch.compile, XLA, custom operators, kernel fusion, memory bandwidth optimization, profiling with Nsight, NCCL, NVLink, collective communication, model parallelism, INT8/FP8 quantization, mixed-precision techniques, large-scale training infrastructure, fault tolerance, cluster orchestration","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":280000,"maxValue":850000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_690339e7-e86"},"title":"Senior Software Engineer, Autonomy - Calibration, Mapping & Localization","description":"<p>About Cyngn</p>\n<p>Based in Mountain View, CA, Cyngn is a publicly-traded autonomous technology company. We deploy self-driving industrial vehicles like forklifts and tuggers to factories, warehouses, and other facilities throughout North America.</p>\n<p>To build this emergent technology, we are looking for innovative, motivated, and experienced leaders to join us and move this field forward. If you like to build, tinker, and create with a team of trusted and passionate colleagues, then Cyngn is the place for you.</p>\n<p>Key reasons to join Cyngn:</p>\n<p>We are small and big. With under 100 employees, Cyngn operates with the energy of a startup. On the other hand, we’re publicly traded. This means our employees not only work in close-knit teams with mentorship from company leaders,they also get access to the liquidity of our publicly-traded equity.</p>\n<p>We build today and deploy tomorrow. Our autonomous vehicles aren’t just test concepts,they’re deployed to real clients right now. That means your work will have a tangible, visible impact.</p>\n<p>We aren’t robots. We just develop them. We’re a welcoming, diverse team of sharp thinkers and kind humans. Collaboration and trust drive our creative environment. At Cyngn, everyone’s perspective matters,and that’s what powers our innovation.</p>\n<p>About this role:</p>\n<p>As a Staff/Senior Software Engineer on our Calibration, Localization, &amp; Mapping (CLAM) team, you will be responsible for delivering mission-critical improvements and new features to our calibration, localization, and mapping subsystems. You will work on a small, highly focused team developing production-quality software that enables efficient and accurate creation of HD maps at Cyngn deployment-sites and robust localization for Cyngn’s autonomous vehicle fleets.</p>\n<p>Responsibilities</p>\n<ul>\n<li><p>Design, implement, tune, and test mapping, localization, and sensor calibration algorithms for our autonomous vehicle platforms using C++ and Python.</p>\n</li>\n<li><p>Develop tooling and metrics for performance validation and continuous testing frameworks.</p>\n</li>\n<li><p>Balance project tasks, code reviews, and research to meet product-driven milestones in a fast-paced startup environment.</p>\n</li>\n</ul>\n<p>Qualifications</p>\n<ul>\n<li><p>MS/Phd with focus in robotics or a similar technical field of study</p>\n</li>\n<li><p>Solid foundation in probability theory, linear algebra, 3D geometry, and spatial coordinate transformations.</p>\n</li>\n<li><p>In-depth understanding of matrix factorization algorithms and Lie algebra/groups.</p>\n</li>\n<li><p>Solid theoretical knowledge of state-of-the-art techniques in 3D Lidar-based mapping and localization for autonomous vehicles (LOAM series, GICP, FastLIO, bundle-adjustment)</p>\n</li>\n<li><p>Familiarity with state estimation frameworks such as EKF’s as well as modern nonlinear optimization libraries (GTSAM, G2O, Ceres-Solver, GNC-Solver, etc.)</p>\n</li>\n<li><p>6+ years of industry experience as an autonomous vehicle or robotics software engineering professional including hands-on implementation and tuning on production hardware.</p>\n</li>\n<li><p>6+ years industry experience writing C++ software in a production environment - architecture design, unit testing, code review, algorithm performance trade-offs, etc.</p>\n</li>\n<li><p>Proficiency in Python.</p>\n</li>\n<li><p>Excellent written &amp; verbal communication skills.</p>\n</li>\n</ul>\n<p>Bonus Qualifications</p>\n<ul>\n<li><p>Proven record of top-tier publications or patents.</p>\n</li>\n<li><p>Experience with GPU programming, CUDA.</p>\n</li>\n<li><p>Experience in implementing automated map change detection and updating techniques.</p>\n</li>\n<li><p>Experience implementing modern multi-sensor calibration and sensor mis-alignment detection algorithms.</p>\n</li>\n<li><p>Experience with camera-based SLAM and 3D multi-view geometry.</p>\n</li>\n<li><p>Experience working with ROS2 to design, build, and operate robotic systems.</p>\n</li>\n<li><p>Exposure to modern software development version control and project management tools - Git, Jira, etc.</p>\n</li>\n</ul>\n<p>Benefits &amp; Perks</p>\n<ul>\n<li><p>Health benefits (Medical, Dental, Vision, HSA and FSA (Health &amp; Dependent Daycare), Employee Assistance Program, 1:1 Health Concierge)</p>\n</li>\n<li><p>Life, Short-term and long-term disability insurance (Cyngn funds 100% of premiums)</p>\n</li>\n<li><p>Company 401(k)</p>\n</li>\n<li><p>Commuter Benefits</p>\n</li>\n<li><p>Flexible vacation policy</p>\n</li>\n<li><p>Sabbatical leave opportunity after 5 years with the company</p>\n</li>\n<li><p>Paid Parental Leave</p>\n</li>\n<li><p>Daily lunches for in-office employees and fully-stocked kitchen with snacks and beverages</p>\n</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_690339e7-e86","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Cyngn","sameAs":"https://www.cyngn.com/","logo":"https://logos.yubhub.co/cyngn.com.png"},"x-apply-url":"https://jobs.lever.co/cyngn/716dbe41-cac5-4d23-9ec3-cc05b32322b4","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$180,000-198,000 per year","x-skills-required":["C++","Python","Probability theory","Linear algebra","3D geometry","Spatial coordinate transformations","Matrix factorization algorithms","Lie algebra/groups","State estimation frameworks","Nonlinear optimization libraries"],"x-skills-preferred":["GPU programming","CUDA","Automated map change detection and updating techniques","Modern multi-sensor calibration and sensor mis-alignment detection algorithms","Camera-based SLAM and 3D multi-view geometry","ROS2","Git","Jira"],"datePosted":"2026-04-17T12:28:37.248Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Mountain View"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"C++, Python, Probability theory, Linear algebra, 3D geometry, Spatial coordinate transformations, Matrix factorization algorithms, Lie algebra/groups, State estimation frameworks, Nonlinear optimization libraries, GPU programming, CUDA, Automated map change detection and updating techniques, Modern multi-sensor calibration and sensor mis-alignment detection algorithms, Camera-based SLAM and 3D multi-view geometry, ROS2, Git, Jira","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":180000,"maxValue":198000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_1662ffb6-3c9"},"title":"R&D Engineering, Sr Staff Engineer","description":"<p>You will work as a senior staff engineer in the R&amp;D engineering team at Synopsys. As a member of this team, you will be responsible for architecting and optimizing high-performance simulation kernels for the Synopsys VCS RTL simulator using advanced C++ techniques. You will also explore and implement GPU acceleration strategies with CUDA to significantly reduce simulation runtimes for customers. Additionally, you will leverage deep knowledge of Verilog/SystemVerilog LRM to ensure accurate and reliable simulation across diverse design environments.</p>\n<p>Your responsibilities will include:</p>\n<ul>\n<li>Architecting and optimizing high-performance simulation kernels for the Synopsys VCS RTL simulator using advanced C++ techniques.</li>\n<li>Exploring and implementing GPU acceleration strategies with CUDA to significantly reduce simulation runtimes for customers.</li>\n<li>Leveraging deep knowledge of Verilog/SystemVerilog LRM to ensure accurate and reliable simulation across diverse design environments.</li>\n<li>Integrating AI-powered tools (such as Cursor, GitHub Copilot, and generative AI assistants) to automate code generation and debugging processes.</li>\n<li>Mentoring and guiding junior engineers, fostering skills development and technical growth within the team.</li>\n<li>Collaborating with distributed R&amp;D teams to maintain Synopsys&#39; leadership and drive innovation in the EDA industry.</li>\n</ul>\n<p>As a senior staff engineer, you will have a significant impact on the company&#39;s success. 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We lead in chip design, verification, and IP integration, empowering the creation of high-performance silicon chips and software content.</p>\n<p><strong>You Are:</strong></p>\n<p>You are a highly skilled and passionate Staff R&amp;D Engineer with a strong background in programming, algorithms, and electronics. You have a keen interest in developing advanced circuit simulators and thrive in a collaborative, innovative environment. You possess excellent problem-solving skills and have a solid understanding of electronic circuit operations. Your experience in C/C++ based multi-thread programming and GPU programming and Linux-based environments makes you a perfect fit for our team. You are eager to contribute to the development of PrimeSim and PrimeSim HTV, bringing your expertise to help us solve some of the semiconductor design industry’s most challenging problems.</p>\n<p>You have at least 7+ years (5+ years for Master’s degree holder) of relevant experience, making you a sophisticated professional ready to make a significant impact.</p>\n<p><strong>What You’ll Be Doing:</strong></p>\n<ul>\n<li>Developing and implementing advanced circuit simulators, PrimeSim Pro and PrimeSim HTV.</li>\n<li>Collaborating with cross-functional teams to enhance the performance and capabilities of our EDA software stack.</li>\n<li>Utilizing your programming skills in C/C++ to create efficient and robust software solutions.</li>\n<li>Applying your knowledge of data structures and algorithms to optimize circuit simulation processes.</li>\n<li>Conducting thorough testing and debugging to ensure the reliability and accuracy of the simulators.</li>\n<li>Staying updated with the latest industry trends and technologies to continuously improve our products.</li>\n</ul>\n<p><strong>The Impact You Will Have:</strong></p>\n<ul>\n<li>Contributing to the development of cutting-edge simulation tools that drive the future of semiconductor design.</li>\n<li>Enabling our customers to bring high-performance chip designs to market rapidly and efficiently.</li>\n<li>Enhancing the productivity and capabilities of our EDA software stack through your innovative solutions.</li>\n<li>Supporting the advancement of AI engines and data analytics within our products.</li>\n<li>Helping to solve high-value problems in the semiconductor industry, including multi-die system integration and rapid node migration.</li>\n<li>Playing a critical role in ensuring the reliability and success of our customers&#39; silicon lifecycle management.</li>\n</ul>\n<p><strong>What You’ll Need:</strong></p>\n<ul>\n<li>Strong programming skills in C/C++.</li>\n<li>In-depth knowledge of data structures and algorithms.</li>\n<li>Excellent problem-solving skills and analytical thinking.</li>\n<li>Understanding of electronic circuit operation.</li>\n<li>2+ years of GPU programming experience (preferred)</li>\n<li>Experience with multi-thread programming (preferred).</li>\n<li>Proficiency in Linux-based environments (preferred).</li>\n</ul>\n<p><strong>Who You Are:</strong></p>\n<p>A dedicated, detail-oriented engineer with a passion for innovation and excellence. You are a team player with strong communication skills, capable of collaborating effectively with colleagues across various disciplines. Your proactive approach and continuous learning mindset enable you to stay ahead in a fast-paced, evolving industry. You are committed to delivering high-quality solutions and contributing to the success of both your team and the organization.</p>\n<p><strong>The Team You’ll Be A Part Of:</strong></p>\n<p>You will be joining a dynamic and forward-thinking R&amp;D team dedicated to developing state-of-the-art circuit simulators. Our team focuses on solving high-value problems in the semiconductor design industry, leveraging the full EDA software stack and integrating hyperconvergence, AI engines, and data analytics. Together, we drive the innovations that enable our customers to achieve their goals and push the boundaries of technology.</p>\n<p><strong>Rewards and Benefits:</strong></p>\n<p>We offer a comprehensive range of health, wellness, and financial benefits to cater to your needs. Our total rewards include both monetary and non-monetary offerings. Your recruiter will provide more details about the salary range and benefits during the hiring process.</p>\n<p>At Synopsys, we want talented people of every background to feel valued and supported to do their best work. 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As a GPU Performance Engineer, you&#39;ll architect and implement the foundational systems that power Claude and push the frontiers of what&#39;s possible with large language models. You&#39;ll be responsible for maximizing GPU utilization and performance at unprecedented scale, developing cutting-edge optimizations that directly enable new model capabilities and dramatically improve inference efficiency.</p>\n<p>Working at the intersection of hardware and software, you&#39;ll implement state-of-the-art techniques from custom kernel development to distributed system architectures. Your work will span the entire stack—from low-level tensor core optimizations to orchestrating thousands of GPUs in perfect synchronization.</p>\n<p>Strong candidates will have a track record of delivering transformative GPU performance improvements in production ML systems and will be excited to shape the future of AI infrastructure alongside world-class researchers and engineers.</p>\n<p><strong>You might be a good fit if you:</strong></p>\n<ul>\n<li>Have deep experience with GPU programming and optimization at scale</li>\n<li>Are impact-driven, passionate about delivering measurable performance breakthroughs</li>\n<li>Can navigate complex systems from hardware interfaces to high-level ML frameworks</li>\n<li>Enjoy collaborative problem-solving and pair programming</li>\n<li>Want to work on state-of-the-art language models with real-world impact</li>\n<li>Care about the societal impacts of your work</li>\n<li>Thrive in ambiguous environments where you define the path forward</li>\n</ul>\n<p><strong>Strong candidates may also have experience with:</strong></p>\n<ul>\n<li>GPU Kernel Development: CUDA, Triton, CUTLASS, Flash Attention, tensor core optimization</li>\n<li>ML Compilers &amp; Frameworks: PyTorch/JAX internals, torch.compile, XLA, custom operators</li>\n<li>Performance Engineering: Kernel fusion, memory bandwidth optimization, profiling with Nsight</li>\n<li>Distributed Systems: NCCL, NVLink, collective communication, model parallelism</li>\n<li>Low-Precision: INT8/FP8 quantization, mixed-precision techniques</li>\n<li>Production Systems: Large-scale training infrastructure, fault tolerance, cluster orchestration</li>\n</ul>\n<p><strong>Representative projects:</strong></p>\n<ul>\n<li>Co-design attention mechanisms and algorithms for next-generation hardware architectures</li>\n<li>Develop custom kernels for emerging quantization formats and mixed-precision techniques</li>\n<li>Design distributed communication strategies for multi-node GPU clusters</li>\n<li>Optimize end-to-end training and inference pipelines for frontier language models</li>\n<li>Build performance modeling frameworks to predict and optimize GPU utilization</li>\n<li>Implement kernel fusion strategies to minimize memory bandwidth bottlenecks</li>\n<li>Create resilient systems for planet-scale distributed training infrastructure</li>\n<li>Profile and eliminate performance bottlenecks in production serving infrastructure</li>\n<li>Partner with hardware vendors to influence future accelerator capabilities and software stacks</li>\n</ul>\n<p><strong>Deadline to apply:</strong> None. Applications will be reviewed on a rolling basis.</p>\n<p>The expected salary range for this position is:</p>\n<p>Annual Salary: $280,000 - $850,000USD</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_11a60d5a-f54","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/4926227008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$280,000 - $850,000USD","x-skills-required":["GPU programming","optimization at scale","custom kernel development","distributed system architectures","low-level tensor core optimizations","orchestrating thousands of GPUs","GPU kernel development","CUDA","Triton","CUTLASS","Flash Attention","tensor core optimization","ML compilers & frameworks","PyTorch/JAX internals","torch.compile","XLA","custom operators","performance engineering","kernel fusion","memory bandwidth optimization","profiling with Nsight","distributed systems","NCCL","NVLink","collective communication","model parallelism","low-precision","INT8/FP8 quantization","mixed-precision techniques","production systems","large-scale training infrastructure","fault tolerance","cluster orchestration"],"x-skills-preferred":["GPU programming","optimization at scale","custom kernel development","distributed system architectures","low-level tensor core optimizations","orchestrating thousands of GPUs","GPU kernel development","CUDA","Triton","CUTLASS","Flash Attention","tensor core optimization","ML compilers & frameworks","PyTorch/JAX internals","torch.compile","XLA","custom operators","performance engineering","kernel fusion","memory bandwidth optimization","profiling with Nsight","distributed systems","NCCL","NVLink","collective communication","model parallelism","low-precision","INT8/FP8 quantization","mixed-precision techniques","production systems","large-scale training infrastructure","fault tolerance","cluster orchestration"],"datePosted":"2026-03-08T13:45:05.412Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA | New York City, NY | Seattle, WA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"GPU programming, optimization at scale, custom kernel development, distributed system architectures, low-level tensor core optimizations, orchestrating thousands of GPUs, GPU kernel development, CUDA, Triton, CUTLASS, Flash Attention, tensor core optimization, ML compilers & frameworks, PyTorch/JAX internals, torch.compile, XLA, custom operators, performance engineering, kernel fusion, memory bandwidth optimization, profiling with Nsight, distributed systems, NCCL, NVLink, collective communication, model parallelism, low-precision, INT8/FP8 quantization, mixed-precision techniques, production systems, large-scale training infrastructure, fault tolerance, cluster orchestration, GPU programming, optimization at scale, custom kernel development, distributed system architectures, low-level tensor core optimizations, orchestrating thousands of GPUs, GPU kernel development, CUDA, Triton, CUTLASS, Flash Attention, tensor core optimization, ML compilers & frameworks, PyTorch/JAX internals, torch.compile, XLA, custom operators, performance engineering, kernel fusion, memory bandwidth optimization, profiling with Nsight, distributed systems, NCCL, NVLink, collective communication, model parallelism, low-precision, INT8/FP8 quantization, mixed-precision techniques, production systems, large-scale training infrastructure, fault tolerance, cluster orchestration","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":280000,"maxValue":850000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_fed192b6-e3e"},"title":"Training: ML Framework Engineer","description":"<p><strong>Training: ML Framework 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>Department</strong></p>\n<p>Scaling</p>\n<p><strong>Compensation</strong></p>\n<ul>\n<li>$205K – $445K • 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<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>Training Runtime designs the core distributed machine-learning training runtime that powers everything from early research experiments to frontier-scale model runs. 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