{"version":"0.1","company":{"name":"YubHub","url":"https://yubhub.co","jobsUrl":"https://yubhub.co/jobs/city/us-wa-bellevue/"},"x-facet":{"type":"city","slug":"us-wa-bellevue","display":"Us-wa-bellevue","count":2},"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_10be6d91-db3"},"title":"Senior Software Engineer - Identity, Data Security and Trust","description":"<p>At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work.</p>\n<p>We are hiring a Senior Software Engineer across our Identity, Data Security and Trust organization. These teams build the security backbone of Snowflake , enabling customers to confidently bring their most sensitive data and workloads to the Data Cloud, empowering every Snowflake engineer to deliver the most secure platform at scale, and giving customers the tools to monitor, achieve, and maintain a strong security, governance, privacy, and compliance posture.</p>\n<p><strong>Responsibilities</strong></p>\n<p>As a Senior Software Engineer at Snowflake, you will:</p>\n<ul>\n<li>Design and implement critical AI security capabilities , including controlled, audited agent workflows, MCP server and client security, agent identity frameworks, and admin guardrails , that form the foundation for secure agentic enterprise adoption</li>\n<li>Build and evolve foundational identity and access management systems, including authentication protocols, SSO integrations, multi-factor authentication, OAuth/OIDC, SAML, SCIM, and fine-grained RBAC that scales to millions of objects and users</li>\n<li>Design and develop core security infrastructure spanning secret management, key management, service identity, and end-to-end encryption across a natively multi-cloud environment</li>\n<li>Build and maintain security tooling to define, monitor, enforce, and detect policy violations across the platform; implement automation and self-service processes that increase developer autonomy and promote secure-by-default engineering</li>\n<li>Design and implement extensible, plug-and-play platform components that enable first-, second-, and third-party security detectors, responders, and visualizations to be built on top of Snowflake&#39;s Trust Center</li>\n<li>Leverage industry-accredited benchmarks (e.g., CIS) to develop standards-based security posture assessments; contribute to behavioral analytics pipelines and PII attribution capabilities that help detect, prevent, and respond to threats and abuse vectors</li>\n<li>Partner with engineering teams across the company to understand security pain points and deliver solutions that reduce friction without compromising safety</li>\n<li>Lead the design and enforcement of secure network architectures to actively defend against ransomware attacks and detect sophisticated data exfiltration attempts.</li>\n<li>Participate in design reviews, code reviews, and on-call rotations; hold a high bar for code quality, reliability, and sound technical decisions</li>\n</ul>\n<p><strong>Requirements</strong></p>\n<p>Our ideal Senior Software Engineer will have:</p>\n<ul>\n<li>5+ years of industry experience designing, building, and operating large-scale distributed systems in production cloud environments</li>\n<li>Strong foundational computer science skills , data structures, algorithms, systems design, and distributed computing</li>\n<li>Proficiency in one or more of Java, C++, Python, Golang, or Rust, with solid SQL skills for data-driven features</li>\n<li>Experience shipping and on-calling production services with a focus on availability, reliability, and observability</li>\n<li>A collaborative, low-ego approach to engineering , comfortable iterating quickly and working across PM, design, and peer engineering teams</li>\n<li>BS in Computer Science, Computer Engineering, or a related technical discipline, or equivalent practical experience</li>\n</ul>\n<p><strong>Nice to Have</strong></p>\n<p>Bonus points for the following:</p>\n<ul>\n<li>Knowledge of identity and access management concepts and protocols , SAML, SCIM, OAuth, OIDC, Federation, MFA, or RBAC</li>\n<li>Familiarity with security infrastructure domains such as secret management, key management, service identity, authentication, or authorization</li>\n<li>Experience with database internals, query engines, or data platform systems</li>\n<li>Exposure to machine learning or AI systems, particularly applied to anomaly detection, behavioral analytics, or risk classification</li>\n<li>Contributions to developer platforms, SDKs, or multi-tenant extensibility frameworks</li>\n<li>Experience deploying and operating services on Kubernetes or production cloud platforms (AWS, Azure, or GCP)</li>\n</ul>\n<p><strong>Why Join Us?</strong></p>\n<p>These teams sit at a rare intersection: the security, identity, infrastructure, and customer trust layers of one of the fastest-growing data platforms in the world. You won&#39;t be maintaining someone else&#39;s security layer; you&#39;ll be defining it. These are teams that value craft, curiosity, and collaboration, and that take pride in making complex security invisible to the developers and customers who depend on it. If you want a role where your technical decisions matter and your growth is tied directly to the scale of the platform you&#39;re protecting , and you&#39;re energized by the challenge of securing the agentic era before it fully arrives , this is it.</p>\n<p>Snowflake is growing fast, and we’re scaling our team to help enable and accelerate our growth. We are looking for people who share our values, challenge ordinary thinking, and push the pace of innovation while building a future for themselves and Snowflake.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">Job Data by <a href=\"https://yubhub.co\">YubHub</a>.</p>","url":"https://jobs.ashbyhq.com/snowflake/5f731aa9-26a6-4760-8941-9f4aff96864d","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Snowflake","sameAs":"https://careers.snowflake.com/","logo":"https://logos.yubhub.co/careers.snowflake.com.png"},"x-apply-url":"https://jobs.ashbyhq.com/snowflake/5f731aa9-26a6-4760-8941-9f4aff96864d","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$200K – $287.5K","x-skills-required":["Java","C++","Python","Golang","Rust","SQL","distributed systems","data structures","algorithms","systems design","computer science"],"x-skills-preferred":["SAML","SCIM","OAuth","OIDC","Federation","MFA","RBAC","secret management","key management","service identity","authentication","authorization","database internals","query engines","data platform systems","machine learning","AI systems","anomaly detection","behavioral analytics","risk classification","Kubernetes","AWS","Azure","GCP"],"x-benefits":[],"x-licences":[],"x-visa-sponsorship":"unknown","x-visa-sponsorship-text":null,"x-work-authorisation":"unknown","x-work-authorisation-text":null,"datePosted":"2026-10-28T10:46:32.609Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"US-WA-Bellevue; US-CA-Menlo Park"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Java, C++, Python, Golang, Rust, SQL, distributed systems, data structures, algorithms, systems design, computer science, SAML, SCIM, OAuth, OIDC, Federation, MFA, RBAC, secret management, key management, service identity, authentication, authorization, database internals, query engines, data platform systems, machine learning, AI systems, anomaly detection, behavioral analytics, risk classification, Kubernetes, AWS, Azure, GCP","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":200000,"maxValue":287500,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_7d5d3c13-bf5"},"title":"Senior/Staff System Research Engineer – LLM Inference Optimization","description":"<p>At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work.</p>\n<p>We are looking for talented systems developers and researchers to join the Snowflake AI Research team and advance the state of the art in LLM inference systems and optimization.</p>\n<p>Our mission is to build the next generation of high-performance and intelligent inference systems. We optimize not only how fast and efficiently models run, but also how quickly inference systems can adapt to new models, architectures, hardware, and workloads.</p>\n<p>Our work spans the full inference stack,from distributed serving and runtime systems to GPU kernels and model-system co-design. We explore techniques such as adaptive parallelism, speculative and parallel decoding, disaggregated inference, scheduling and batching, KV-cache optimization, model swapping, quantization, and GPU kernel optimization to push the frontier of latency, throughput, scalability, and cost.</p>\n<p>Beyond optimizing individual models, we are building intelligent and adaptive inference systems that can automate performance optimization,rapidly profiling new models and workloads, identifying bottlenecks, selecting effective execution strategies, and adapting system configurations with minimal manual tuning. We embrace AI-native engineering, using AI not only as the workload we optimize, but also as a tool to accelerate system development, experimentation, debugging, optimization, and adaptation to new models. Our goal is to accelerate both the speed of inference and the agility of inference development.</p>\n<p>This is an exciting opportunity to collaborate with a world-class team, including founding members of DeepSpeed, vLLM, and TensorFlow. Together, we will push the boundaries of AI systems and bring cutting-edge research into production-scale AI.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Design and develop high-performance LLM inference systems, spanning distributed serving, runtime systems, GPU execution, and performance-critical kernels.</li>\n<li>Develop novel techniques to improve inference latency, generation speed, throughput, memory efficiency, scalability, and cost.</li>\n<li>Explore advanced inference techniques including speculative and parallel decoding, prefill/decode disaggregation, adaptive parallelism, continuous batching and scheduling, KV-cache management, quantization, and communication optimization.</li>\n<li>Develop adaptive and intelligent inference systems that automatically optimize execution for new model architectures, hardware platforms, workload characteristics, and deployment environments.</li>\n<li>Apply AI-driven and AI-native approaches to systems engineering, including automated profiling, bottleneck identification, configuration search, code generation, experimentation, runtime strategy selection, debugging, and performance tuning.</li>\n<li>Independently identify high-impact performance and systems problems, formulate hypotheses, prototype solutions, and drive promising ideas from research through production.</li>\n<li>Design distributed inference strategies across GPUs and nodes, including tensor, sequence, pipeline, data, and expert parallelism.</li>\n<li>Develop efficient approaches for multi-model serving, dynamic resource management, model loading and swapping, and workload-aware scheduling.</li>\n<li>Analyze and optimize GPU kernels and operators for attention, MoE, communication, and other performance-critical model components.</li>\n<li>Explore model-system co-design, including model or post-training techniques that unlock substantially more efficient inference.</li>\n<li>Profile and benchmark end-to-end workloads to identify bottlenecks across compute, memory, communication, networking, scheduling, and model execution.</li>\n<li>Collaborate closely with model researchers, infrastructure teams, and product teams to deploy research innovations in production.</li>\n<li>Open-source and publish innovations through technical blogs and top-tier systems and machine learning conferences.</li>\n</ul>\n<p><strong>Requirements</strong></p>\n<ul>\n<li>Bachelor’s degree in Computer Science, Electrical Engineering, or a related field. A Master’s degree or PhD is preferred.</li>\n<li>5+ years of experience in one or more of the following areas: LLM inference systems, distributed AI systems, GPU systems, or high-performance computing.</li>\n<li>Strong understanding of modern LLM inference architectures and the performance tradeoffs involved in serving large-scale models.</li>\n<li>Hands-on experience with modern LLM inference and serving frameworks, such as vLLM, SGLang, TensorRT-LLM, or similar systems.</li>\n<li>Experience designing, extending, or optimizing inference runtimes, including areas such as scheduling, batching, KV-cache management, distributed execution, parallelism, speculative decoding, or disaggregated serving.</li>\n<li>Strong understanding of GPU architectures and experience with CUDA, Triton, or similar GPU programming environments.</li>\n<li>Experience with performance-oriented libraries and frameworks such as CUTLASS, cuBLAS, cuDNN, or related technologies.</li>\n<li>Experience profiling and diagnosing end-to-end system performance using Nsight Systems, Nsight Compute, or equivalent tools.</li>\n<li>Demonstrated ability to operate as an independent problem identifier and solver,recognizing important problems with limited direction, defining the right technical questions, and driving solutions through ambiguity.</li>\n<li>Strong ability to work across model, runtime, distributed system, and hardware layers and reason about end-to-end performance tradeoffs.</li>\n<li>Experience using AI-native engineering approaches to accelerate software development, experimentation, debugging, optimization, or system adaptation is a strong plus.</li>\n<li>Excellent communication skills and the ability to collaborate effectively across research, engineering, and product teams.</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">Job Data by <a href=\"https://yubhub.co\">YubHub</a>.</p>","url":"https://jobs.ashbyhq.com/snowflake/9e0ae021-f264-4efb-bc80-26c04bd06df6","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Snowflake","sameAs":"http://careers.snowflake.com/","logo":"https://logos.yubhub.co/careers.snowflake.com.png"},"x-apply-url":"https://jobs.ashbyhq.com/snowflake/9e0ae021-f264-4efb-bc80-26c04bd06df6","x-work-arrangement":"hybrid","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"$236K - $330K","x-skills-required":["LLM inference systems","distributed AI systems","GPU systems","high-performance computing","modern LLM inference architectures","performance tradeoffs","large-scale models","LLM inference and serving frameworks","inference runtimes","GPU architectures","CUDA","Triton","GPU programming environments","performance-oriented libraries","frameworks","CUTLASS","cuBLAS","cuDNN","Nsight Systems","Nsight Compute","system performance","independent problem identifier","solver","technical questions","solutions through ambiguity","end-to-end performance tradeoffs","AI-native engineering approaches"],"x-skills-preferred":["AI-driven approaches"],"x-benefits":[],"x-licences":[],"x-visa-sponsorship":"unknown","x-visa-sponsorship-text":null,"x-work-authorisation":"unknown","x-work-authorisation-text":null,"datePosted":"2026-10-24T22:59:51.898Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"US-WA-Bellevue; US-CA-Menlo Park"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"LLM inference systems, distributed AI systems, GPU systems, high-performance computing, modern LLM inference architectures, performance tradeoffs, large-scale models, LLM inference and serving frameworks, inference runtimes, GPU architectures, CUDA, Triton, GPU programming environments, performance-oriented libraries, frameworks, CUTLASS, cuBLAS, cuDNN, Nsight Systems, Nsight Compute, system performance, independent problem identifier, solver, technical questions, solutions through ambiguity, end-to-end performance tradeoffs, AI-native engineering approaches, AI-driven approaches","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":236000,"maxValue":330000,"unitText":"YEAR"}}}]}