{"version":"0.1","company":{"name":"YubHub","url":"https://yubhub.co","jobsUrl":"https://yubhub.co/jobs/skill/tensorboard"},"x-facet":{"type":"skill","slug":"tensorboard","display":"Tensorboard","count":5},"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_c77545f4-627"},"title":"Staff Machine Learning Scientist","description":"<p>We are seeking a Staff Machine Learning Scientist to help grow the Machine Learning Science team, within the Computational Science department. The ideal candidate has a strong knowledge of artificial intelligence (AI), including machine learning (ML) fundamentals and extensive experience with deep learning (DL) methods, a track record of successfully using these methods to answer complex research questions, the ability to drive independent research and thrive in a highly cross-functional environment.</p>\n<p>They will be responsible for the development of algorithms for early, blood-based detection tests for cancer. They will build on a foundation of ML/DL and statistical skills to develop models for identifying molecular signals from blood. They will also work with computational biologists, molecular biologists and ML engineers to design and drive research experiments, and will have a significant impact on the continued growth of an organisation dedicated to changing the entire landscape of cancer.</p>\n<p>The role reports to the Director, Machine Learning Science. This role can be a Hybrid role based in our Brisbane, California headquarters (2-3 days per week in office), or remote.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Independently pursue cutting-edge research in AI applied to biological problems (including cancer research, genomics, computational biology, immunology, etc.)</li>\n<li>Build new models or fine-tune existing models to identify biological changes resulting from disease</li>\n<li>Build models that achieve high accuracy and that generalise robustly to new data</li>\n<li>Apply contemporary interpretability techniques to provide a deeper understanding of the underlying signal identified by the model, ideally suggesting potential biological mechanisms</li>\n<li>Work closely with ML Engineering partners to ensure that Freenome&#39;s computational infrastructure supports optimal model training and iteration</li>\n<li>Take a mindful, transparent, and humane approach to your work</li>\n</ul>\n<p>Requirements:</p>\n<ul>\n<li>PhD or equivalent research experience with an AI emphasis and in a relevant, quantitative field such as Computer Science, Statistics, Mathematics, Engineering, Computational Biology, or Bioinformatics</li>\n<li>6+ years of post-doc or post-PhD industry experience achieving impactful results using relevant modelling techniques</li>\n<li>Expertise demonstrated by research publications or industry achievements, in driving independent research in applied machine learning, deep learning and complex data modelling</li>\n<li>Practical and theoretical understanding of fundamental ML models like generalised linear models, kernel machines, decision trees and forests, neural networks, boosting and model aggregation</li>\n<li>Practical and theoretical understanding of DL models like large language models or other foundation models</li>\n<li>Extensive experience with training paradigms like supervised learning, self-supervised learning, and contrastive learning</li>\n<li>Proficient in current state of the art in ML/DL approaches in different domains, with an ability to envision their applications in biological data</li>\n<li>Proficiency in a general-purpose programming language: Python, R, Java, C, C++, etc.</li>\n<li>Proficiency in one or more ML frameworks such as; PyTorch, TensorFlow and JAX; and ML platforms like Hugging Face</li>\n<li>Experience in ML analysis and developer tools like TensorBoard, MLflow or Weights &amp; Biases</li>\n<li>Excellent ability to communicate across disciplines, work collaboratively, and make progress in smaller steps via experimental iterations</li>\n<li>Proficient at productive cross-functional scientific communication and collaboration with software engineers and computational biologists</li>\n<li>A passion for innovation and demonstrated initiative in tackling new areas of research</li>\n</ul>\n<p>Nice to have:</p>\n<ul>\n<li>Deep domain-specific experience in computational biology, genomics, proteomics or a related field</li>\n<li>Experience in building DL models for genomic data, with knowledge of state-of-the-art DNA foundation models</li>\n<li>Experience in NGS data analysis and bioinformatic pipelines</li>\n<li>Experience with containerized cloud computing environments such as Docker in GCP, Azure, or AWS</li>\n<li>Experience in a production software engineering environment, including the use of automated regression testing, version control, and deployment systems</li>\n</ul>\n<p>Benefits and additional information:</p>\n<ul>\n<li>The US target range of our base salary for new hires is $199,675.00 - $283,500.00. You will also be eligible to receive equity, cash bonuses, and a full range of medical, financial, and other benefits depending on the position offered. Please note that individual total compensation for this position will be determined at the Company&#39;s sole discretion and may vary based on several factors, including but not limited to, location, skill level, years and depth of relevant experience, and education.</li>\n<li>Freenome is proud to be an equal-opportunity employer, and we value diversity. Freenome does not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, veteran status, or any other status protected under federal, state, or local law.</li>\n<li>Applicants have rights under Federal Employment Laws.</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_c77545f4-627","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Freenome","sameAs":"https://freenome.com/","logo":"https://logos.yubhub.co/freenome.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/freenome/jobs/8215797002","x-work-arrangement":"hybrid","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"$199,675.00 - $283,500.00","x-skills-required":["Artificial Intelligence","Machine Learning","Deep Learning","Computational Biology","Genomics","Immunology","Python","R","Java","C","C++","PyTorch","TensorFlow","JAX","Hugging Face","TensorBoard","MLflow","Weights & Biases"],"x-skills-preferred":[],"datePosted":"2026-04-17T12:35:13.294Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Brisbane, California"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Healthcare","skills":"Artificial Intelligence, Machine Learning, Deep Learning, Computational Biology, Genomics, Immunology, Python, R, Java, C, C++, PyTorch, TensorFlow, JAX, Hugging Face, TensorBoard, MLflow, Weights & Biases","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":199675,"maxValue":283500,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_faec8dc3-4d3"},"title":"Senior Machine Learning Scientist","description":"<p>We are seeking a Senior Machine Learning Scientist to help grow the Machine Learning Science team. The ideal candidate has a strong knowledge of artificial intelligence (AI), including machine learning (ML) fundamentals and extensive experience with deep learning (DL) methods. They will be responsible for the development of algorithms for early, blood-based detection tests for cancer. They will build on a foundation of ML/DL and statistical skills to develop models for identifying molecular signals from blood. They will also work with computational biologists, molecular biologists and ML engineers to design and drive research experiments, and will have a significant impact on the continued growth of an organisation dedicated to changing the entire landscape of cancer.</p>\n<p>The role reports to the Director, Machine Learning Science. This role can be a Hybrid role based in our Brisbane, California headquarters (2-3 days per week in office), or remote.</p>\n<p>Key responsibilities include:</p>\n<ul>\n<li>Independently pursuing cutting-edge research in AI applied to biological problems</li>\n<li>Building new models or fine-tuning existing models to identify biological changes resulting from disease</li>\n<li>Building models that achieve high accuracy and that generalise robustly to new data</li>\n<li>Applying contemporary interpretability techniques to provide a deeper understanding of the underlying signal identified by the model, ideally suggesting potential biological mechanisms</li>\n<li>Working closely with ML Engineering partners to ensure that Freenome&#39;s computational infrastructure supports optimal model training and iteration</li>\n<li>Taking a mindful, transparent, and humane approach to your work</li>\n</ul>\n<p>Requirements include:</p>\n<ul>\n<li>PhD or equivalent research experience with an AI emphasis and in a relevant, quantitative field such as Computer Science, Statistics, Mathematics, Engineering, Computational Biology, or Bioinformatics</li>\n<li>3+ years of postdoc or post-PhD industry experience achieving impactful results using relevant modelling techniques</li>\n<li>Expertise, demonstrated by research publications or industry achievements, in applied machine learning, deep learning and complex data modelling</li>\n<li>Practical and theoretical understanding of fundamental ML models like generalised linear models, kernel machines, decision trees and forests, neural networks</li>\n<li>Practical and theoretical understanding of DL models like large language models or other foundation models</li>\n<li>Extensive experience with training paradigms like supervised learning, self-supervised learning, and contrastive learning</li>\n<li>Proficient in current state of the art in ML/DL approaches in different domains, with an ability to envision their applications in biological data</li>\n<li>Proficiency in a general-purpose programming language: Python, R, Java, C, C++, etc.</li>\n<li>Proficiency in one or more ML frameworks such as; Pytorch, Tensorflow and Jax; and ML platforms like Hugging Face</li>\n<li>Experience in ML analysis and developer tools like TensorBoard, MLflow or Weights &amp; Biases</li>\n<li>Excellent ability to communicate across disciplines, work collaboratively, and make progress in smaller steps via experimental iterations</li>\n<li>A passion for innovation and demonstrated initiative in tackling new areas of research</li>\n</ul>\n<p>Nice to have qualifications include:</p>\n<ul>\n<li>Deep domain-specific experience in computational biology, genomics, proteomics or a related field</li>\n<li>Experience in building DL models for genomic data, with knowledge of state-of-the-art DNA foundation models</li>\n<li>Experience in NGS data analysis and bioinformatic pipelines</li>\n<li>Experience with containerized cloud computing environments such as Docker in GCP, Azure, or AWS</li>\n<li>Experience in a production software engineering environment, including the use of automated regression testing, version control, and deployment systems</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_faec8dc3-4d3","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Freenome","sameAs":"https://freenome.com","logo":"https://logos.yubhub.co/freenome.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/freenome/jobs/7963050002","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$173,775 - $246,750","x-skills-required":["PhD or equivalent research experience","Applied machine learning","Deep learning","Complex data modelling","Generalised linear models","Kernel machines","Decision trees and forests","Neural networks","Large language models","Supervised learning","Self-supervised learning","Contrastive learning","Python","R","Java","C","C++","Pytorch","Tensorflow","Jax","Hugging Face","TensorBoard","MLflow","Weights & Biases"],"x-skills-preferred":[],"datePosted":"2026-04-17T12:35:12.037Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Brisbane, California"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Healthcare","skills":"PhD or equivalent research experience, Applied machine learning, Deep learning, Complex data modelling, Generalised linear models, Kernel machines, Decision trees and forests, Neural networks, Large language models, Supervised learning, Self-supervised learning, Contrastive learning, Python, R, Java, C, C++, Pytorch, Tensorflow, Jax, Hugging Face, TensorBoard, MLflow, Weights & Biases","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":173775,"maxValue":246750,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_2bc207d0-89b"},"title":"Senior Machine Learning Engineer","description":"<p>We are seeking a Senior Machine Learning Research Engineer to join the Machine Learning Science (MLS) team, within the Computational Science department. The ideal candidate has a strong knowledge in designing and building deep learning (DL) pipelines, and expertise in creating reliable, scalable artificial intelligence/machine learning (AI/ML) systems in a cloud environment.</p>\n<p>The MLS team at Freenome develops DL models using massive-scale genomic data that presents significant challenges for current training paradigms. The Senior Machine Learning Research Engineer will primarily be responsible for developing and deploying the infrastructure needed to support development of such DL models: enabling distributed DL pipelines, optimising hardware utilisation for efficient training, and performing model optimisations.</p>\n<p>As part of an interdisciplinary R&amp;D team, they will work in close collaboration with machine learning scientists, computational biologists and software engineers to accelerate the development of state-of-the-art ML/AI models and help Freenome achieve its mission.</p>\n<p>Key responsibilities include:</p>\n<ul>\n<li>Implementing and refining DL pipelines on distributed computing platforms to enhance the speed and efficiency of DL operations, including model training, data handling, model management, and inference.</li>\n<li>Collaborating closely with ML scientists and software engineers to understand current challenges and requirements and ensure that the DL model development pipelines created are perfectly aligned with scientific goals and operational needs.</li>\n<li>Continuously monitoring, evaluating, and optimising DL model training pipelines for performance and scalability.</li>\n<li>Staying up to date with the latest advancements in AI, ML, and related technologies, and quickly learning and adapting new tools and frameworks, if necessary.</li>\n<li>Developing and maintaining robust and reproducible DL pipelines that guarantee that DL pipelines can be reliably executed, maintaining consistency and accuracy of results.</li>\n<li>Driving performance improvements across our stack through profiling, optimisation, and benchmarking. Implementing efficient caching solutions and debugging distributed systems to accelerate both training and evaluation pipelines.</li>\n<li>Acting as a bridge facilitating communication between the engineering and scientific teams, documenting and sharing best practices to foster a culture of learning and continuous improvement.</li>\n</ul>\n<p>Must-haves include:</p>\n<ul>\n<li>MS or equivalent experience in a relevant, quantitative field such as Computer Science, Statistics, Mathematics, Software Engineering, with an emphasis on AI/ML theory and/or practical development.</li>\n<li>5+ years of post-MS industry experience working on developing AI/ML software engineering pipelines.</li>\n<li>Proficiency in a general-purpose programming language: Python (preferred), Java, Julia, C, C++, etc.</li>\n<li>Strong knowledge of ML and DL fundamentals and hands-on experience with machine learning frameworks such as PyTorch, TensorFlow, Jax or Scikit-learn.</li>\n<li>In-depth knowledge of scalable and distributed computing platforms that support complex model training (such as Ray or DeepSpeed) and their integration with ML developer tools like TensorBoard, Wandb, or MLflow.</li>\n<li>Experience with cloud platforms (e.g., AWS, Google Cloud, Azure) and how to deploy and manage AI/ML models and pipelines in a cloud environment.</li>\n<li>Understanding of containerisation technologies (e.g., Docker) and computing resource orchestration tools (e.g., Kubernetes) for deploying scalable ML/AI solutions.</li>\n<li>Proven track record of developing and optimising workflows for training DL models, large language models (LLMs), or similar for problems with high data complexity and volume.</li>\n<li>Experience managing large datasets, including data storage (such as HDFS or Parquet on S3), retrieval, and efficient data processing techniques (via libraries and executors such as PyArrow and Spark).</li>\n<li>Proficiency in version control systems (e.g., Git) and continuous integration/continuous deployment (CI/CD) practices to maintain code quality and automate development workflows.</li>\n<li>Expertise in building and launching large-scale ML frameworks in a scientific environment that supports the needs of a research team.</li>\n<li>Excellent ability to work effectively with cross-functional teams and communicate across disciplines.</li>\n</ul>\n<p>Nice-to-haves include:</p>\n<ul>\n<li>Experience working with large-scale genomics or biological datasets.</li>\n<li>Experience managing multimodal datasets, such as combinations of sequence, text, image, and other data.</li>\n<li>Experience GPU/Accelerator programming and kernel development (such as CUDA, Triton or XLA).</li>\n<li>Experience with infrastructure-as-code and configuration management.</li>\n<li>Experience cultivating MLOps and ML infrastructure best practices, especially around reliability, provisioning and monitoring.</li>\n<li>Strong track record of contributions to relevant DL projects, e.g. on github.</li>\n</ul>\n<p>The US target range of our base salary for new hires is $161,925 - $227,325. You will also be eligible to receive equity, cash bonuses, and a full range of medical, financial, and other benefits depending on the position offered.</p>\n<p>Freenome is proud to be an equal-opportunity employer, and we value diversity. Freenome does not discriminate on the basis of race, colour, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, veteran status, or any other status protected under federal, state, or local law.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_2bc207d0-89b","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Freenome","sameAs":"https://freenome.com/","logo":"https://logos.yubhub.co/freenome.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/freenome/jobs/8013673002","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$161,925 - $227,325","x-skills-required":["Python","Java","Julia","C","C++","PyTorch","TensorFlow","Jax","Scikit-learn","Ray","DeepSpeed","TensorBoard","Wandb","MLflow","AWS","Google Cloud","Azure","Docker","Kubernetes","Git","Continuous Integration/Continuous Deployment"],"x-skills-preferred":["Large-scale genomics or biological datasets","Multimodal datasets","GPU/Accelerator programming and kernel development","Infrastructure-as-code and configuration management","MLOps and ML infrastructure best practices"],"datePosted":"2026-04-17T12:35:01.240Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Brisbane, California"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, Java, Julia, C, C++, PyTorch, TensorFlow, Jax, Scikit-learn, Ray, DeepSpeed, TensorBoard, Wandb, MLflow, AWS, Google Cloud, Azure, Docker, Kubernetes, Git, Continuous Integration/Continuous Deployment, Large-scale genomics or biological datasets, Multimodal datasets, GPU/Accelerator programming and kernel development, Infrastructure-as-code and configuration management, MLOps and ML infrastructure best practices","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":161925,"maxValue":227325,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_7f56054b-d77"},"title":"Principal Software Engineer","description":"<p><strong>Summary</strong></p>\n<p>Microsoft AI are looking for a talented Principal Software Engineer at their Mountain View office. This role sits at the heart of strategic decision-making, driving innovations in AI infrastructure. You&#39;ll work directly with key partners to understand, design, and implement complex inferencing capabilities for state-of-the-art deep learning models.</p>\n<p><strong>About the Role</strong></p>\n<p>As a Principal Software Engineer, you will be responsible for engaging directly with key partners to understand, design, and implement complex inferencing capabilities for state-of-the-art deep learning models. You will work with cutting-edge hardware and software stacks to deliver best-in-class inference performance while optimizing for cost, leveraging open-source projects to advance deep learning applications. You will collaborate with external and internal teams to identify new areas for improvement and contribute to innovations that enhance model performance and deployment.</p>\n<p><strong>Accountabilities</strong></p>\n<ul>\n<li>Engage directly with key partners to understand, design, and implement complex inferencing capabilities for state-of-the-art deep learning models.</li>\n<li>Work with cutting-edge hardware and software stacks to deliver best-in-class inference performance while optimizing for cost.</li>\n</ul>\n<p><strong>The Candidate we&#39;re looking for</strong></p>\n<p><strong>Experience:</strong></p>\n<ul>\n<li>6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python.</li>\n</ul>\n<p><strong>Technical skills:</strong></p>\n<ul>\n<li>Experience with model compression (quantization, distillation, SVD, low-rank methods).</li>\n<li>Experience in building high-throughput inference serving stacks (continuous batching, KV-cache optimizations, routing).</li>\n</ul>\n<p><strong>Personal attributes:</strong></p>\n<ul>\n<li>Solid experience in GPU inference optimization (CUDA, TensorRT, Triton, or custom GPU kernels).</li>\n<li>Proficiency in profiling tools (Nsight, TensorBoard, PyTorch profiler) and ability to identify CPU/GPU bottlenecks.</li>\n</ul>\n<p><strong>Benefits</strong></p>\n<ul>\n<li>Competitive salary range of USD $139,900 – $274,800 per year.</li>\n<li>Comprehensive benefits package, including health insurance, retirement plan, and paid time off.</li>\n<li>Opportunities for professional growth and development.</li>\n<li>Collaborative and dynamic work environment.</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_7f56054b-d77","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Microsoft AI","sameAs":"https://microsoft.ai","logo":"https://logos.yubhub.co/microsoft.ai.png"},"x-apply-url":"https://microsoft.ai/job/principal-software-engineer-24/","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"USD $139,900 – $274,800 per year","x-skills-required":["C","C++","C#","Java","JavaScript","Python","model compression","GPU inference optimization"],"x-skills-preferred":["TensorRT","Triton","CUDA","Nsight","TensorBoard","PyTorch profiler"],"datePosted":"2026-03-06T07:30:21.077Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Mountain View"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"C, C++, C#, Java, JavaScript, Python, model compression, GPU inference optimization, TensorRT, Triton, CUDA, Nsight, TensorBoard, PyTorch profiler","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":139900,"maxValue":274800,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_a15b11dd-765"},"title":"Principal Software Engineer","description":"<p><strong>Summary</strong></p>\n<p>Microsoft AI are looking for a talented Principal Software Engineer at their Redmond office. This role sits at the heart of strategic decision-making, turning market data into actionable insights for a company that&#39;s revolutionising AI technology. You&#39;ll work directly with leadership to shape the company&#39;s direction in the AI market.</p>\n<p><strong>About the Role</strong></p>\n<p>As a Principal Software Engineer, you will be responsible for designing and implementing complex software systems that drive innovation in AI infrastructure. You will work with cutting-edge hardware and software stacks to deliver best-in-class inference performance while optimizing for cost, leveraging open-source projects to advance deep learning applications. You will collaborate with external and internal teams to identify new areas for improvement and contribute to innovations that enhance model performance and deployment.</p>\n<p><strong>Accountabilities</strong></p>\n<ul>\n<li>Engage directly with key partners to understand, design, and implement complex inferencing capabilities for state-of-the-art deep learning models, driving innovations in AI infrastructure.</li>\n<li>Work with cutting-edge hardware and software stacks to deliver best-in-class inference performance while optimizing for cost, leveraging open-source projects to advance deep learning applications.</li>\n</ul>\n<p><strong>The Candidate we&#39;re looking for</strong></p>\n<p><strong>Experience:</strong></p>\n<ul>\n<li>Bachelor’s Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.</li>\n</ul>\n<p><strong>Technical skills:</strong></p>\n<ul>\n<li>Experience with model compression (quantization, distillation, SVD, low-rank methods).</li>\n<li>Experience in building high-throughput inference serving stacks (continuous batching, KV-cache optimizations, routing).</li>\n</ul>\n<p><strong>Personal attributes:</strong></p>\n<ul>\n<li>Solid experience in GPU inference optimization (CUDA, TensorRT, Triton, or custom GPU kernels).</li>\n<li>Proficiency in profiling tools (Nsight, TensorBoard, PyTorch profiler) and ability to identify CPU/GPU bottlenecks.</li>\n</ul>\n<p><strong>Benefits</strong></p>\n<ul>\n<li>Competitive salary</li>\n<li>Comprehensive benefits package</li>\n<li>Opportunities for professional growth and development</li>\n<li>Collaborative and dynamic work environment</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_a15b11dd-765","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Microsoft AI","sameAs":"https://microsoft.ai","logo":"https://logos.yubhub.co/microsoft.ai.png"},"x-apply-url":"https://microsoft.ai/job/principal-software-engineer-23/","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"USD $139,900 – $274,800 per year","x-skills-required":["C","C++","C#","Java","JavaScript","Python","model compression","GPU inference optimization","profiling tools"],"x-skills-preferred":["TensorRT","Triton","CUDA","TensorBoard","PyTorch profiler"],"datePosted":"2026-03-06T07:29:39.108Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Redmond"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"C, C++, C#, Java, JavaScript, Python, model compression, GPU inference optimization, profiling tools, TensorRT, Triton, CUDA, TensorBoard, PyTorch profiler","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":139900,"maxValue":274800,"unitText":"YEAR"}}}]}