{"version":"0.1","company":{"name":"YubHub","url":"https://yubhub.co","jobsUrl":"https://yubhub.co/jobs/skill/ecmp"},"x-facet":{"type":"skill","slug":"ecmp","display":"Ecmp","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_ac14f361-5b8"},"title":"Network Engineer, Capacity and Efficiency","description":"<p>We&#39;re looking for a network engineer who thinks in metrics first. You will use deep networking knowledge and rigorous measurement to figure out where and how bandwidth, latency, and dollars are being used, find optimization opportunities and land them.</p>\n<p>You will instrument spine-leaf fabrics, BGP, SDN overlays, and cloud interconnect products well enough to build them. You&#39;ll own the observability and efficiency surface for Anthropic&#39;s network: from per-flow telemetry on backbone routers, to QoS policy on cross-region links carrying inference traffic, to cost attribution that tells a research team exactly what their checkpoint sync is costing.</p>\n<p>This is a hands-on IC role. You&#39;ll write code (Python, Go), build dashboards, model capacity, and ship config changes to production routers. You&#39;ll also influence architecture: when the data says a traffic pattern is pathological, you&#39;ll be in the room root causing it and fixing it.</p>\n<p>You will be working across three areas: network telemetry and observability, traffic engineering, and cost modeling and attribution. 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The team is responsible for container networking, connectivity, and network services that support large-scale, GPU-driven workloads across regions and environments. They focus on scalability, reliability, security, and performance while delivering intuitive platforms for internal teams and customers.</p>\n<p>About the Role</p>\n<p>As a Senior Engineer focused on our Cilium-based CNI, you will design, build, and operate the container networking layer that underpins CoreWeave&#39;s Kubernetes platforms. Day to day, you will work on evolving our CNI stack to support large, high-density GPU clusters with demanding throughput and latency requirements. You will partner closely with Kubernetes, Infrastructure, and Network Services engineers to ensure the platform is highly available, observable, and secure. This role spans architecture, implementation, and operations, with ownership from prototype through production. You will also help shape how our networking platform scales for future growth.</p>\n<p>Who You Are</p>\n<ul>\n<li>5+ years of experience as a Software Engineer or Systems Engineer working on cloud infrastructure or large-scale distributed systems.</li>\n<li>Hands-on production experience with Cilium CNI (or equivalent advanced CNIs), including cluster configuration and lifecycle management.</li>\n<li>Strong understanding of Cilium&#39;s eBPF datapath, policy model, and load-balancing mechanisms.</li>\n<li>Deep knowledge of cloud networking concepts, including VPCs, subnets, routing, security groups/ACLs, NAT, and ingress/egress architectures.</li>\n<li>Experience designing multi-tenant network architectures with strong isolation and security.</li>\n<li>Solid grounding in TCP/IP, dynamic routing (e.g., BGP), ECMP, MTU/fragmentation, and overlay/underlay networking (VXLAN, Geneve, encapsulation).</li>\n<li>Experience with network observability and troubleshooting across L3–L7.</li>\n<li>Proficiency in at least one systems language such as Golang or C/C++.</li>\n<li>Experience working in modern CI/CD environments.</li>\n<li>Experience operating Kubernetes at scale, including cluster lifecycle management and debugging networking issues across pods, nodes, and external services.</li>\n<li>Demonstrated ownership of complex systems end-to-end.</li>\n</ul>\n<p>Preferred</p>\n<ul>\n<li>Experience operating cloud-scale network services across tens of thousands of nodes and multiple regions.</li>\n<li>Contributions to Cilium, Kubernetes, or related open-source networking projects.</li>\n<li>Experience with eBPF development and performance tuning.</li>\n<li>Experience building Kubernetes operators or controllers.</li>\n<li>Familiarity with service meshes, multi-cluster networking, or cluster mesh solutions.</li>\n<li>Experience in GPU-heavy, HPC, or other performance-sensitive environments.</li>\n</ul>\n<p>Wondering if you’re a good fit?</p>\n<p>We believe in investing in our people and value candidates who bring diverse experiences , even if you’re not a 100% match on paper. If some of this sounds like you, we’d love to talk.</p>\n<ul>\n<li>You love solving complex distributed systems and networking challenges at scale.</li>\n<li>You’re curious about cloud-native networking, eBPF, and Kubernetes internals.</li>\n<li>You’re an expert in building reliable, scalable infrastructure that runs in production.</li>\n</ul>\n<p>Why CoreWeave?</p>\n<p>At CoreWeave, we work hard, have fun, and move fast! We’re in an exciting stage of hyper-growth that you will not want to miss out on. We’re not afraid of a little chaos, and we’re constantly learning. Our team cares deeply about how we build our product and how we work together, which is represented through our core values:</p>\n<ul>\n<li>Be Curious at Your Core</li>\n<li>Act Like an Owner</li>\n<li>Empower Employees</li>\n<li>Deliver Best-in-Class Client Experiences</li>\n<li>Achieve More Together</li>\n</ul>\n<p>The base salary range for this role is $165,000 to $242,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility).</p>\n<p>What We Offer</p>\n<p>The range we’ve posted represents the typical compensation range for this role. To determine actual compensation, we review the market rate for each candidate which can include a variety of factors. These include qualifications, experience, interview performance, and location. In addition to a competitive salary, we offer a variety of benefits to support your needs, including:</p>\n<ul>\n<li>Medical, dental, and vision insurance</li>\n<li>100% paid for by CoreWeave</li>\n<li>Company-paid Life Insurance</li>\n<li>Voluntary supplemental life insurance</li>\n<li>Short and long-term disability insurance</li>\n<li>Flexible Spending Account</li>\n<li>Health Savings Account</li>\n<li>Tuition Reimbursement</li>\n<li>Ability to Participate in Employee Stock Purchase Program (ESPP)</li>\n<li>Mental Wellness Benefits through Spring Health</li>\n<li>Family-Forming support provided by Carrot</li>\n<li>Paid Parental Leave</li>\n<li>Flexible, full-service childcare support with Kinside</li>\n<li>401(k) with a generous employer match</li>\n<li>Flexible PTO</li>\n<li>Catered lunch each day in our office and data center locations</li>\n<li>A casual work environment</li>\n<li>A work culture focused on innovative disruption</li>\n</ul>\n<p>Our Workplace</p>\n<p>While we prioritize a hybrid work environment, remote work may be considered for candidates located more than 30 miles from an office, based on role requirements for specialized skill sets. New hires will be invited to attend onboarding at one of our hubs within their first month. Teams also gather quarterly to support collaboration.</p>\n<p>California Consumer Privacy Act - California applicants only</p>\n<p>CoreWeave is an equal opportunity employer, committed to fostering an inclusive and supportive workplace. All qualified applicants and candidates will receive consideration for employment without regard to race, color, religion, sex, disability, age, sexual orientation, gender identity, national origin, veteran status, or genetic information. As part of this commitment and consistent with the Americans with Disabilities Act (ADA), CoreWeave will ensure that qualified applicants and candidates with disabilities are provided reasonable accommodations for the hiring process, unless such accommodation would cause an undue hardship. If reasonable accommodation is needed, please contact: careers@coreweave.com.</p>\n<p>Export Control Compliance</p>\n<p>This position requires access to export controlled information. To conform to U.S. Government export regulations applicable to that information, applicant must either be (A) a U.S. person, defined as a (i) U.S. citizen or national, (ii) U.S. lawful permanent resident (green card holder), (iii) refugee under 8 U.S.C. § 1157, or (iv) asylee under 8 U.S.C. § 1158, (B) eligible to access the export controlled information without a required export authorization, or (C) eligible and reasonably likely to obtain the required export authorization from the applicable U.S. government agency. 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You will use deep networking knowledge and rigorous measurement to figure out where and how bandwidth, latency, and dollars are being used, find optimization opportunities and land them.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Build the network observability stack. Design and deploy telemetry pipelines , sFlow/IPFIX, gNMI streaming, eBPF host probes , that turn packet counters into per-flow, per-tenant, per-workload cost and utilization data. Own the SLIs for backbone and DCN fabric health.</li>\n<li>Hunt for efficiency. Analyze inter-region traffic patterns, identify hot links and stranded capacity, and quantify the dollar impact. Build the models that tell us whether we should buy more capacity, or move the workload.</li>\n<li>Own QoS and traffic engineering. Design and operate traffic classification, marking, and shaping across the backbone. Make sure bulk checkpoint transfers don’t starve latency-sensitive inference, and that we’re not paying premium cross-region rates for traffic that could take the cheap path.</li>\n<li>Drive cost attribution. Tie network spend , egress, interconnect ports, transit, optical leases , back to the teams and workloads that generate it. Make network cost a first-class input to capacity planning and workload placement decisions.</li>\n<li>Influence decisions you don&#39;t own. A large fraction of this role is convincing other teams to act on what your data shows: making the case to research that a traffic pattern needs to change, to finance that an interconnect tranche is worth buying, to Systems Networking that a QoS policy needs rewriting.</li>\n</ul>\n<p><strong>Requirements</strong></p>\n<ul>\n<li>Have 5+ years operating large-scale production networks , data center fabrics (spine-leaf, Clos), backbone/WAN, or hyperscaler-adjacent environments.</li>\n<li>Are genuinely fluent across the stack: BGP (including policy and communities), ECMP, VXLAN/EVPN or equivalent overlays, QoS (DSCP, queuing, shaping), and L1/optical basics (DWDM, coherent, LAGs).</li>\n<li>Know at least one major CSP’s networking model deeply , AWS (VPC, TGW, Direct Connect, Gateway Load Balancer) or GCP (Shared VPC, Interconnect, Cloud Router, Network Connectivity Center) , and understand how their overlays interact with physical underlays.</li>\n<li>Have built or operated network telemetry at scale: streaming telemetry (gNMI/OpenConfig), flow export (sFlow, IPFIX, NetFlow), or eBPF-based host-side instrumentation. You can reason about sampling, cardinality, and storage tradeoffs.</li>\n<li>Comfortable writing Python or Go to build tooling, telemetry pipelines, infrastructure-as-code, config management for network devices and automation, that you’ll ship to production.</li>\n<li>Think quantitatively by default. You reach for a notebook or a Grafana query before you reach for an opinion, and you can turn messy counter data into a defensible cost model.</li>\n<li>Communicate crisply. You can explain to a finance partner why a 10% egress reduction matters, and to a network engineer why a specific ECMP imbalance is costing real money.</li>\n</ul>\n<p><strong>Nice to Have</strong></p>\n<ul>\n<li>SRE experience for large-scale network infrastructure , designing for reliability, defining SLOs/SLIs for network services, capacity planning with error budgets, and incident response for network-impacting outages at scale.</li>\n<li>Background on a cloud provider&#39;s networking team or a cloud networking product team , building or operating the interconnect, backbone, or SDN control plane from the provider side, not just consuming it as a customer.</li>\n<li>Familiarity with AI/ML infrastructure traffic patterns like collective communication (all-reduce, all-gather), checkpoint/weight transfer, inference serving, and how these stress networks differ than traditional workloads in terms of burst behavior, flow synchronization, and bandwidth symmetry.</li>\n<li>Experience with HPC fabrics like InfiniBand, RoCE v2, lossless Ethernet, or custom high-radix topologies and an understanding of how job placement, congestion management, and adaptive routing interact at scale.</li>\n<li>Background in traffic engineering for large backbones and the operational judgment to know when TE is worth the complexity.</li>\n<li>Hands-on time with multi-cloud connectivity: cross-cloud peering, private interconnect products, and the billing models that come with them.</li>\n<li>Experience building cost/chargeback systems for shared infrastructure, or FinOps exposure in a large cloud environment.</li>\n</ul>\n<p><strong>Representative Projects</strong></p>\n<ul>\n<li>Build a per-flow cost attribution pipeline that traces every byte of cross-region egress back to the team and workload that generated it</li>\n<li>Design QoS policy for the private backbone that prevents bulk checkpoint transfers from starving inference traffic</li>\n<li>Model whether it&#39;s cheaper to buy an additional 1.6Tb interconnect tranche or to re-route traffic through existing capacity</li>\n<li>Instrument DCN fabric utilization with streaming telemetry and build the Grafana dashboards that become the team&#39;s source of truth for network observability</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_9cd0420a-99d","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://anthropic.com","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/5177143008","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["network engineering","network observability","telemetry pipelines","sFlow/IPFIX","gNMI streaming","eBPF host probes","BGP","ECMP","VXLAN/EVPN","QoS","DSCP","queuing","shaping","L1/optical basics","DWDM","coherent","LAGs","AWS","GCP","cloud networking","infrastructure-as-code","config management","automation","Python","Go","quantitative analysis","cost modeling","communication"],"x-skills-preferred":["SRE","cloud provider's networking team","cloud networking product team","AI/ML infrastructure traffic patterns","HPC fabrics","traffic engineering","multi-cloud connectivity","cost/chargeback systems","FinOps"],"datePosted":"2026-04-18T15:42:29.482Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA | New York City, NY"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"network engineering, network observability, telemetry pipelines, sFlow/IPFIX, gNMI streaming, eBPF host probes, BGP, ECMP, VXLAN/EVPN, QoS, DSCP, queuing, shaping, L1/optical basics, DWDM, coherent, LAGs, AWS, GCP, cloud networking, infrastructure-as-code, config management, automation, Python, Go, quantitative analysis, cost modeling, communication, SRE, cloud provider's networking team, cloud networking product team, AI/ML infrastructure traffic patterns, HPC fabrics, traffic engineering, multi-cloud connectivity, cost/chargeback systems, FinOps"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_a6f2cc66-67b"},"title":"Networking Operating System Firmware Engineer","description":"<p><strong>Networking Operating System Firmware Engineer</strong></p>\n<p><strong>About the Team</strong></p>\n<p>OpenAI’s Hardware organization develops silicon and system-level solutions designed for the unique demands of advanced AI workloads. The team is responsible for building the next generation of AI-native silicon while working closely with software and research partners to co-design hardware tightly integrated with AI models. In addition to delivering production-grade silicon for OpenAI’s supercomputing infrastructure, the team also creates custom design tools and methodologies that accelerate innovation and enable hardware optimized specifically for AI.</p>\n<p><strong>About the Role</strong></p>\n<p>We’re seeking a Networking Operating System Firmware Engineer to help bootstrap and scale the switching layer of our AI supercomputers. In this role, you’ll build and maintain custom SONiC NOS images from scratch, working across the Linux kernel, switch ASIC SAI/SDKs, platform drivers, control-plane services, and orchestration layers.</p>\n<p>You will validate, configure, and optimize switch platforms used across our high-bandwidth cluster fabric, ensuring performance, reliability, availability, and seamless integration with fleet automation. You’ll collaborate with hardware and systems teams and guide vendors to meet stringent technical expectations.</p>\n<p>This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.</p>\n<p><strong>In this role, you will:</strong></p>\n<ul>\n<li>Design, develop, and maintain custom SONiC NOS images for large-scale bleeding-edge AI fabrics.</li>\n</ul>\n<ul>\n<li>Integrate and configure Linux kernel components, device drivers, switch ASIC SDKs, and SAI layers.</li>\n</ul>\n<ul>\n<li>Bring up new switch platforms (thermal/fan control, power monitoring, transceiver management, watchdogs, OSFP CMIS, LEDs, CPLDs, etc.).</li>\n</ul>\n<ul>\n<li>Extend and customize SONiC services for routing, telemetry, control-plane state, and distributed automation.</li>\n</ul>\n<ul>\n<li>Work with hardware teams to validate ASIC configurations, link bring-up, SerDes tuning, buffer profiles, and performance baselines.</li>\n</ul>\n<ul>\n<li>Evaluate switch silicon SDK releases, track vendor deliverables, and define platform requirements with vendors and ASIC partners.</li>\n</ul>\n<ul>\n<li>Debug complex issues spanning kernel, platform drivers, SONiC dockers, routing agents, orchestration services, hardware signals, and network topology.</li>\n</ul>\n<ul>\n<li>Integrate switches into fleet-wide monitoring, remote diagnostics, telemetry pipelines, and automated lifecycle workflows.</li>\n</ul>\n<ul>\n<li>Develop robust CI/build pipelines for reproducible NOS builds and controlled rollout across the fleet.</li>\n</ul>\n<ul>\n<li>Support factory bring-up and qualification all the way through mass deployment.</li>\n</ul>\n<ul>\n<li>Collaborate, architect, implement, and deploy novel networking protocols and technologies to achieve maximum performance and reliability at AI factory scale.</li>\n</ul>\n<p><strong>You might thrive in this role if you:</strong></p>\n<ul>\n<li>Proven experience working with SONiC or comparable NOS stacks (FBOSS, Cumulus Linux, Arista EOS, Junos PFE-level integration, etc.).</li>\n</ul>\n<ul>\n<li>Experience with updating OpenConfig gNMI interfaces and YANG data models.</li>\n</ul>\n<ul>\n<li>Strong background in Linux kernel, network device drivers, and low-level OS internals.</li>\n</ul>\n<ul>\n<li>Experience integrating Broadcom / Marvell / NVIDIA / Intel ASIC SDKs and SAI implementations.</li>\n</ul>\n<ul>\n<li>Proficiency in C, C++ and Python; familiarity with Rust/Go is a plus.</li>\n</ul>\n<ul>\n<li>Deep understanding of L2/L3 forwarding, ECMP, RoCE, BGP, QoS, PFC, buffer tuning, and telemetry.</li>\n</ul>\n<ul>\n<li>Hands-on experience with hardware platform bring-up and board-level debugging.</li>\n</ul>\n<ul>\n<li>Familiarity with CI/CD pipelines, distributed config/state management, and large-scale automation.</li>\n</ul>\n<ul>\n<li>Strong cross-functional problem solving in high-performance, distributed environments.</li>\n</ul>\n<ul>\n<li>Ability to lead teams to deliver a project end to end.</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_a6f2cc66-67b","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/582b878e-61bf-4be2-8b30-623434baf726","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$266K – $445K","x-skills-required":["SONiC","Linux kernel","network device drivers","low-level OS internals","C","C++","Python","Rust/Go","L2/L3 forwarding","ECMP","RoCE","BGP","QoS","PFC","buffer tuning","telemetry"],"x-skills-preferred":["OpenConfig gNMI interfaces","YANG data models","Broadcom / Marvell / NVIDIA / Intel ASIC SDKs","SAI implementations","CI/CD pipelines","distributed config/state management","large-scale automation"],"datePosted":"2026-03-06T18:29:41.466Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"SONiC, Linux kernel, network device drivers, low-level OS internals, C, C++, Python, Rust/Go, L2/L3 forwarding, ECMP, RoCE, BGP, QoS, PFC, buffer tuning, telemetry, OpenConfig gNMI interfaces, YANG data models, Broadcom / Marvell / NVIDIA / Intel ASIC SDKs, SAI implementations, CI/CD pipelines, distributed config/state management, large-scale automation","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":266000,"maxValue":445000,"unitText":"YEAR"}}}]}