About the role\\nEvery request that hits Claude , from claude.ai, the API, our cloud partners, or internal research , passes through a routing decision. Not a generic load balancer round-robin, but a decision that accounts for what's already cached where, which accelerator the request runs best on, and what else is in flight across the fleet.\\n\\nGet it right and you extract meaningfully more throughput from the same hardware. Get it wrong and you burn capacity, miss latency SLOs, or shed load that shouldn't have been shed.\\n\\nThe Inference Routing team owns this layer. We build the cluster-level routing and coordination plane for Anthropic's inference fleet , the system that sits between the API surface and the inference engines themselves, making fleet-wide efficiency decisions in real time.\\n\\nAs Anthropic moves from "many independent inference replicas" toward "a single warehouse-scale computer running a coordinated program," Dystro is the coordination layer. This is a deeply technical team.\\n\\nThe engineers here design custom load-balancing algorithms, build quantitative models of system performance, debug latency spikes that cross kernel, network, and framework boundaries, and reason carefully about cache placement across thousands of accelerators.\\n\\nThey work shoulder-to-shoulder with teams that write kernels and ML framework internals.\\n\\nThe EM for this team doesn't need to write kernels , but they do need the systems depth to make architectural calls, evaluate deeply technical candidates, and spot when a proposed optimization will have second-order effects on the fleet.\\n\\nYou'll inherit a strong team of distributed-systems engineers, and you'll be accountable for two things that pull in different directions: shipping system-level performance improvements that measurably increase fleet throughput and efficiency, and running the team operationally so that deploys are safe, incidents are rare, and the teams who depend on Dystro can plan around you with confidence.\\n\\nThe job is holding both.\\n\\n## Representative work:\\nThings the Inference Routing EM actually spends time on:\\n- Deciding whether a proposed routing algorithm change is worth the deploy risk, given the modeled throughput gain and the blast radius if it regresses\\n- Sequencing a quarter where KV-cache offload, a new coordination protocol, and two model launches all compete for the same engineers\\n- Working through a persistent tail-latency regression with the team , walking down from fleet-level metrics to per-replica behavior to a root cause in the networking stack\\n- Building the case (with numbers) to peer teams for why a cross-team protocol change unlocks the next efficiency win\\n- Running the post-incident review after a cache-eviction bug caused a capacity event, and turning it into process changes that stick\\n- Interviewing a candidate who has built schedulers at supercomputing scale, and deciding whether they'd be additive to a team that already goes deep\\n\\n## What you'll do:\\nDrive system-level performance\\n- Own the technical roadmap for cluster-level inference efficiency , routing decisions, cache placement and eviction, cross-replica coordination, and the protocols that keep routing and inference engines in sync\\n- Partner with the inference engine, kernels, and performance teams to identify fleet-level throughput and latency wins, then turn those into shipped improvements with measurable results\\n- Build the team's habit of quantitative performance modeling: claim a win only when you can measure it, and know before you ship what the expected effect is\\n\\nDeliver reliably and operate cleanly\\n- Set technical strategy for how routing evolves across heterogeneous hardware (GPUs, TPUs, Trainium) and across all our serving surfaces\\n- Run the team's operational backbone , on-call rotation, incident response, postmortem review, deploy safety , so the team can ship aggressively without the system becoming fragile\\n- Create clarity at a seam: Inference Routing sits between the API surface, the inference engines, and the cloud deployment teams. You'll make sure commitments are realistic, dependencies are understood, and nobody is surprised\\n\\nBuild and grow the team\\n- Develop and retain a strong existing team, and hire against the bar described above: people who can go to the OS and framework level when the problem demands it, and who care about production reliability\\n- Coach engineers through a roadmap where priorities shift with model launches, new hardware, and scaling demands. We pair a lot here , you'll help make that collaboration pattern productive\\n- Pick up slack when it matters. This is a small team in a critical path; sometimes the EM is the one unblocking a stuck deploy or synthesizing a design debate\\n\\n## You may be a good fit if you:\\n- Have 5+ years of engineering management experience, ideally with at least part of that leading teams on critical-path production infrastructure at scale\\n- Have a deep systems background , load balancing, scheduling, cache-coherent distributed state, high-performance networking, or similar. You need enough depth to make architectural calls about routing and efficiency, and to evaluate candidates who go to the kernel and framework level\\n- Have shipped performance improvements in large-scale systems and can explain, with numbers, what the impact was\\n- Have run production infrastructure with real operational stakes: on-call, incident response, capacity events, deploy discipline\\n- Are results-oriented with a bias toward impact, and comfortable working in a space where throughput, latency, stability, and feature velocity all pull in different directions\\n- Build strong relationships across team boundaries , this is a seam role, and much of the job is making sure other teams can rely on yours\\n- Are curious about machine learning systems. You don't need an ML research background, but you should want to learn how transformer inference actually works and how that shapes the systems problems\\n\\nStrong candidates may also have:\\n- Experience with LLM inference serving , KV caching, continuous batching, request scheduling, prefill/decode disaggregation\\n- Background in cluster schedulers, load balancers, service meshes, or coordination planes at scale\\n- Familiarity with heterogeneous accelerator fleets (GPU/TPU/Trainium) and how hardware differences affect workload placement\\n- Experience with GPU/accelerator programming, ML framework internals, or OS-level performance debugging , enough to follow and evaluate the technical work, not necessarily to do it daily\\n- Led teams at supercomputing or hyperscaler infrastructure scale\\n- Led teams through rapid-growth periods where hiring and onboarding competed with roadmap delivery\\n\\nThe annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\\nAnnual Salary: $405,000-$485,000 USD
\nXML job scraping automation by YubHub
","url":"https://yubhub.co/jobs/job_ac45e205-e7d","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/5155391008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$405,000-$485,000 USD","x-skills-required":["engineering management","distributed systems","load balancing","scheduling","cache-coherent distributed state","high-performance networking","machine learning systems"],"x-skills-preferred":["LLM inference serving","cluster schedulers","load balancers","service meshes","coordination planes","heterogeneous accelerator fleets","GPU/TPU/Trainium","GPU/accelerator programming","ML framework internals","OS-level performance debugging"],"datePosted":"2026-04-18T15:56:48.587Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA | New York City, NY"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"engineering management, distributed systems, load balancing, scheduling, cache-coherent distributed state, high-performance networking, machine learning systems, LLM inference serving, cluster schedulers, load balancers, service meshes, coordination planes, heterogeneous accelerator fleets, GPU/TPU/Trainium, GPU/accelerator programming, ML framework internals, OS-level performance debugging","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":405000,"maxValue":485000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_9cd0420a-99d"},"title":"Network Engineer, Capacity and Efficiency","description":"About the Role
\nWe'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.
\nResponsibilities
\nRequirements
\nNice to Have
\nRepresentative Projects
\nXML job scraping automation by YubHub
","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_63af8568-789"},"title":"Engineering Manager, Inference Routing and Performance","description":"About the role\\nEvery request that hits Claude , from claude.ai, the API, our cloud partners, or internal research , passes through a routing decision. Not a generic load balancer round-robin, but a decision that accounts for what's already cached where, which accelerator the request runs best on, and what else is in flight across the fleet.\\n\\nGet it right and you extract meaningfully more throughput from the same hardware. Get it wrong and you burn capacity, miss latency SLOs, or shed load that shouldn't have been shed.\\n\\nThe Inference Routing team owns this layer. We build the cluster-level routing and coordination plane for Anthropic's inference fleet , the system that sits between the API surface and the inference engines themselves, making fleet-wide efficiency decisions in real time.\\n\\nAs Anthropic moves from "many independent inference replicas" toward "a single warehouse-scale computer running a coordinated program," Dystro is the coordination layer. This is a deeply technical team.\\n\\nThe engineers here design custom load-balancing algorithms, build quantitative models of system performance, debug latency spikes that cross kernel, network, and framework boundaries, and reason carefully about cache placement across thousands of accelerators.\\n\\nThey work shoulder-to-shoulder with teams that write kernels and ML framework internals.\\n\\nThe EM for this team doesn't need to write kernels , but they do need the systems depth to make architectural calls, evaluate deeply technical candidates, and spot when a proposed optimization will have second-order effects on the fleet.\\n\\nYou'll inherit a strong team of distributed-systems engineers, and you'll be accountable for two things that pull in different directions: shipping system-level performance improvements that measurably increase fleet throughput and efficiency, and running the team operationally so that deploys are safe, incidents are rare, and the teams who depend on Dystro can plan around you with confidence.\\n\\nThe job is holding both.\\n\\n## Representative work:\\nThings the Inference Routing EM actually spends time on:\\n- Deciding whether a proposed routing algorithm change is worth the deploy risk, given the modeled throughput gain and the blast radius if it regresses\\n- Sequencing a quarter where KV-cache offload, a new coordination protocol, and two model launches all compete for the same engineers\\n- Working through a persistent tail-latency regression with the team , walking down from fleet-level metrics to per-replica behavior to a root cause in the networking stack\\n- Building the case (with numbers) to peer teams for why a cross-team protocol change unlocks the next efficiency win\\n- Running the post-incident review after a cache-eviction bug caused a capacity event, and turning it into process changes that stick\\n- Interviewing a candidate who has built schedulers at supercomputing scale, and deciding whether they'd be additive to a team that already goes deep\\n\\n## What you'll do:\\nDrive system-level performance\\n- Own the technical roadmap for cluster-level inference efficiency , routing decisions, cache placement and eviction, cross-replica coordination, and the protocols that keep routing and inference engines in sync\\n- Partner with the inference engine, kernels, and performance teams to identify fleet-level throughput and latency wins, then turn those into shipped improvements with measurable results\\n- Build the team's habit of quantitative performance modeling: claim a win only when you can measure it, and know before you ship what the expected effect is\\n\\nDeliver reliably and operate cleanly\\n- Set technical strategy for how routing evolves across heterogeneous hardware (GPUs, TPUs, Trainium) and across all our serving surfaces\\n- Run the team's operational backbone , on-call rotation, incident response, postmortem review, deploy safety , so the team can ship aggressively without the system becoming fragile\\n- Create clarity at a seam: Inference Routing sits between the API surface, the inference engines, and the cloud deployment teams. You'll make sure commitments are realistic, dependencies are understood, and nobody is surprised\\n\\nBuild and grow the team\\n- Develop and retain a strong existing team, and hire against the bar described above: people who can go to the OS and framework level when the problem demands it, and who care about production reliability\\n- Coach engineers through a roadmap where priorities shift with model launches, new hardware, and scaling demands. We pair a lot here , you'll help make that collaboration pattern productive\\n- Pick up slack when it matters. This is a small team in a critical path; sometimes the EM is the one unblocking a stuck deploy or synthesizing a design debate\\n\\n## You may be a good fit if you:\\n- Have 5+ years of engineering management experience, ideally with at least part of that leading teams on critical-path production infrastructure at scale\\n- Have a deep systems background , load balancing, scheduling, cache-coherent distributed state, high-performance networking, or similar. You need enough depth to make architectural calls about routing and efficiency, and to evaluate candidates who go to the kernel and framework level\\n- Have shipped performance improvements in large-scale systems and can explain, with numbers, what the impact was\\n- Have run production infrastructure with real operational stakes: on-call, incident response, capacity events, deploy discipline\\n- Are results-oriented with a bias toward impact, and comfortable working in a space where throughput, latency, stability, and feature velocity all pull in different directions\\n- Build strong relationships across team boundaries , this is a seam role, and much of the job is making sure other teams can rely on yours\\n- Are curious about machine learning systems. You don't need an ML research background, but you should want to learn how transformer inference actually works and how that shapes the systems problems\\n\\nStrong candidates may also have:\\n- Experience with LLM inference serving , KV caching, continuous batching, request scheduling, prefill/decode disaggregation\\n- Background in cluster schedulers, load balancers, service meshes, or coordination planes at scale\\n- Familiarity with heterogeneous accelerator fleets (GPU/TPU/Trainium) and how hardware differences affect workload placement\\n- Experience with GPU/accelerator programming, ML framework internals, or OS-level performance debugging , enough to follow and evaluate the technical work, not necessarily to do it daily\\n- Led teams at supercomputing or hyperscaler infrastructure scale\\n- Led teams through rapid-growth periods where hiring and onboarding competed with roadmap delivery\\n\\nThe annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\\nAnnual Salary: $405,000-$485,000 USD
\nXML job scraping automation by YubHub
","url":"https://yubhub.co/jobs/job_63af8568-789","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/5155391008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$405,000-$485,000 USD","x-skills-required":["engineering management","deep systems background","load balancing","scheduling","cache-coherent distributed state","high-performance networking"],"x-skills-preferred":["LLM inference serving","cluster schedulers","load balancers","service meshes","coordination planes","heterogeneous accelerator fleets","GPU/TPU/Trainium","GPU/accelerator programming","ML framework internals","OS-level performance debugging"],"datePosted":"2026-04-18T15:37:38.038Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA | New York City, NY"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"engineering management, deep systems background, load balancing, scheduling, cache-coherent distributed state, high-performance networking, LLM inference serving, cluster schedulers, load balancers, service meshes, coordination planes, heterogeneous accelerator fleets, GPU/TPU/Trainium, GPU/accelerator programming, ML framework internals, OS-level performance debugging","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":405000,"maxValue":485000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_94e4ded2-2fd"},"title":"Quantum Engineer","description":"As a Quantum Engineer at Rigetti Computing, you will contribute to the development of our gate-based, superconducting quantum computers. Your work will involve characterizing experimental devices, assessing their performance, identifying areas for improvement, and providing feedback to internal development teams.
\nKey responsibilities include:
\nProviding technical focus and leadership on a particular R&D topic, such as gate calibration, qubit relaxation and/or dephasing, piloting new research ideas in small processors, integration of new ideas into large processors, or implementation of quantum error correction.
\nCollaborating across diverse teams of expert engineers to turn experimental concepts into robust technologies that work at large QPU scales.
\nStaying current with cutting-edge research, synthesizing results from the literature, and implementing state-of-the-art calibration, error diagnostic, and system optimization techniques to enhance device performance.
\nRequired qualifications include:
\nPhD in Physics or similar applied scientific field, or Masters' in Physics with 2+ years relevant industry experience.
\nDemonstrated abilities in experimental problem solving and experimental research.
\nExpertise in the practical implementation of coherent quantum effects and control.
\nProficiency using Python in a scientific context.
\nAbility to thrive in a strongly collaborative environment, with a team-first mindset.
\nNice to have qualifications include:
\nExperience calibrating and characterizing single- and two-qubit quantum gates in superconducting quantum devices.
\nExperience utilizing quantum simulation frameworks, such as QuTip.
\nProven capability to execute and effectively communicate outcomes from innovative research and development initiatives.
\nSoftware development experience in a collaborative industry setting.
\nPrevious technical role in an industrial or startup setting.
\nAdditional information:
\nAs engineering leaders, we value diversity and are committed to building a culture of inclusion to attract and engage innovative thinkers.
\nApplications from women, minorities, and other under-represented groups are encouraged.
\nXML job scraping automation by YubHub
","url":"https://yubhub.co/jobs/job_94e4ded2-2fd","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Rigetti Computing","sameAs":"https://www.rigetti.com","logo":"https://logos.yubhub.co/rigetti.com.png"},"x-apply-url":"https://jobs.lever.co/rigetti/0f66c1c3-12be-4e60-a7e1-25e494ee2841","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["Python","Experimental problem solving","Experimental research","Coherent quantum effects and control","Quantum simulation frameworks"],"x-skills-preferred":["Calibrating and characterizing single- and two-qubit quantum gates","Utilizing quantum simulation frameworks","Software development","Previous technical role in an industrial or startup setting"],"datePosted":"2026-04-17T12:54:13.460Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Mountain View"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, Experimental problem solving, Experimental research, Coherent quantum effects and control, Quantum simulation frameworks, Calibrating and characterizing single- and two-qubit quantum gates, Utilizing quantum simulation frameworks, Software development, Previous technical role in an industrial or startup setting"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_b4d3cb52-7c4"},"title":"Senior ASIC Verification Engineer, Coherent High Speed Interconnect","description":"We are now looking for a Senior ASIC Verification Engineer for our Coherent High Speed Interconnect team. For two decades, NVIDIA has pioneered visual computing, the art and science of computer graphics. With our invention of the GPU - the engine of modern visual computing - the field has grown to encompass video games, movie production, product design, medical diagnosis, and scientific research.
\nToday, we stand at the beginning of the next era, the AI computing era, ignited by a new computing model, GPU deep learning. This new model - where deep neural networks are trained to recognize patterns from meaningful amounts of data - has shown to be deeply effective at solving the most sophisticated problems in everyday life.
\nAs a Senior ASIC Verification Engineer at NVIDIA, you will verify the design and implementation of our innovative high speed coherent interconnects for our mobile SoCs and GPUs. This position offers the opportunity to have real impact in a multifaceted, technology-focused company impacting product lines ranging from consumer graphics to self-driving cars and the growing field of artificial intelligence.
\nResponsibilities:
\nRequirements:
\nNVIDIA is widely considered to be one of the technology world’s most desirable employers! We have some of the most forward-thinking and dedicated people in the world working for us. If you're creative and autonomous, we want to hear from you.
\nYou will also be eligible for equity and benefits.
\nApplications for this job will be accepted at least until March 13, 2026.
\nThis posting is for an existing vacancy.
\nNVIDIA uses AI tools in its recruiting processes.
\nXML job scraping automation by YubHub
","url":"https://yubhub.co/jobs/job_b4d3cb52-7c4","directApply":true,"hiringOrganization":{"@type":"Organization","name":"NVIDIA","sameAs":"https://nvidia.wd5.myworkdayjobs.com","logo":"https://logos.yubhub.co/nvidia.com.png"},"x-apply-url":"https://nvidia.wd5.myworkdayjobs.com/en-US/NVIDIAExternalCareerSite/job/US-CA-Santa-Clara/Senior-ASIC-Verification-Engineer--Coherent-High-Speed-Interconnect_JR2010025","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["Verification of high-speed coherent interconnect design, architecture and golden models","Micro-architecture using sophisticated verification methodologies","Testbenches, BFMs, Checkers, Monitors","System Verilog","C++ programming language","Design and verification tools (VCS or equivalent simulation tools, debug tools like Debussy, GDB)"],"x-skills-preferred":["Random stimulus along with functional coverage and assertion-based verification methodologies","Prior Design or Verification experience of Coherent high-speed interconnects","Knowledge of industry standard interconnect protocols like PCIE, CXL, CHI"],"datePosted":"2026-03-09T20:46:52.056Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"US, CA, Santa ClaraUS, MA, WestfordUS, TX, AustinUS, OR, Hillsboro"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Verification of high-speed coherent interconnect design, architecture and golden models, Micro-architecture using sophisticated verification methodologies, Testbenches, BFMs, Checkers, Monitors, System Verilog, C++ programming language, Design and verification tools (VCS or equivalent simulation tools, debug tools like Debussy, GDB), Random stimulus along with functional coverage and assertion-based verification methodologies, Prior Design or Verification experience of Coherent high-speed interconnects, Knowledge of industry standard interconnect protocols like PCIE, CXL, CHI"}]}