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
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\nYour primary focus will be on delivering an Airbnb network platform that is flexible, efficient, always available, and scales with the needs of the business. You will work closely with peers across Cloud Infra, Security, Reliability, and many other partner teams across the company to achieve this goal.
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\nAs a successful candidate, you will have a strong background in engineering management, with 2+ years of experience and 8+ years of relevant software development experience in a fast-paced tech environment. You will also have experience with a public cloud provider (AWS, GCP, Azure) and their networking service offerings, as well as experience running large-scale networking systems and software (e.g. proxies, DNS, gateways).
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\nYou will document and share your knowledge with the rest of the organization and our customers using Knowledge Centered-Services (KCS) methodology. You will also have a mindset of continuous improvement, in terms of efficiency of support processes and customer satisfaction.
\nTo be successful in this role, you will need to work across multi-cultural and geographically distributed teams. You will have 3+ years of proven experience in Technical Support in a Software business, a technical background in fields like Information Technology, Network Engineering, Software Engineering, and a 'Customer First' mindset.
\nYou will be a team player, able to work in a fast-paced environment with a positive and adaptable approach. You will have knowledge of databases (SQL / No SQL) or search software technologies, experience with SaaS and/or Distributed systems, experience with Linux/Unix, experience with APIs, familiarity with Knowledge Centered-Services (KCS), and highly collaborative.
\nNative Korean language skills and professional working proficiency in English are required, as well as effective verbal and written communication skills.
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\nAs a Staff Product Manager, Networking, you will own the strategy and roadmap for CoreWeave's advanced networking product portfolio. On a day-to-day basis, you will translate market insights, customer needs, and technical constraints into clear product requirements and execution plans. You will work closely with cross-functional partners to launch new products and evolve existing offerings, ensuring they meet CoreWeave's high standards for performance, scalability, and reliability.
\nThis is a highly visible role with significant influence over the future of CoreWeave's networking platform.
\nCoreWeave is a rapidly growing company that prioritizes innovation and disruption. We believe in investing in our people and value candidates who can bring their own diversified experiences to our teams.
\nIf you love defining product strategy in technically complex, fast-evolving domains, are curious about emerging networking technologies, and are an expert at turning market insights and customer needs into scalable, high-impact products, then we'd love to talk.
\nAt 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. 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 reflected in our core values:
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\nxAI's mission is to create AI systems that can accurately understand the universe and aid humanity in its pursuit of knowledge.
\nAbout the Role
\nGrok and X are powered largely from our own on-premise infrastructure which enables us to move at speed and efficiency when deploying vast amounts of capacity. People wouldn't be able to enjoy participating in the townhall on X or use Grok to understand the universe if it weren't for our Edge networking infrastructure.
\nWe are seeking two senior engineers that help architect, develop, and build our peering and transit infrastructure with associated routing policies, eDNS, cloud connectivity, CGNAT, and load balancer fleets. The two successful candidates will have great Python skills to remove repetitive engineering cycles and auto-mitigate customer impacts.
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\nReporting to the Manager of Partner Delivery Success, this role will be responsible for developing partner delivery capability within a defined product domain and regional partner portfolio. This role ensures service delivery partners are prepared to successfully deploy Okta solutions through capability development, go-live validation, and delivery oversight.
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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
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\nEmployment Type
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\nAbout the Team
\nThe Technical Support team is responsible for ensuring that developers and enterprises can reliably build mission critical solutions using OpenAI models. We provide technical guidance, resolve complex issues and support customers in maximizing value and adoption from deploying our highly-capable models. We work closely with Technical Success, Product, Engineering and others to deliver the best possible experience to our customers at scale. We think from an automation-first mindset and leverage the latest in AI to scale our support operations. Join the Senior Support Engineering (SSE) team at OpenAI and help shape the future of Technical Support in the age of AI.
\nAbout the Role
\nWe are looking for a Senior Support Engineer to collaborate directly with our strategic enterprise accounts and product teams, helping solve some of the most difficult problems faced by our Customers. You will be part of the best technical troubleshooting team at OpenAI, and our Customers and Engineering teams will look to you for technical guidance in addressing the most technically difficult issues in our environment.
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\nThe nature of this role will be low volume, high difficulty.
\nThis role is based in Dublin, Ireland. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.
\nIn this role, you will:
\nYou might thrive in this role if you:
\nCompensation, Benefits and Perks
\nThis is a position with OpenAI Ireland Ltd., which controls the hiring and management of this position.
\nTotal compensation includes an annual salary, generous equity, and benefits.
\n#LI-NM2
\nAbout OpenAI
\nOpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.
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