# jobs Published articles for jobs. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## AI Changed How Spotify Builds. What We Learned (and Fixed) About Quality at Higher Velocity DevFeed: [AI Changed How Spotify Builds. What We Learned (and Fixed) About Quality at Higher Velocity]() Original publisher: [Read original article]() Author: Spotify Engineering Published: 2026-09-16T19:13:53Z Content type: article Language: en Sources: [Spotify Engineering](), [Spotify Engineering Blog]() Topics: [Monitoring](), [reliability](), [Microservices](), [Data pipelines](), [Artificial Intelligence](), [Job]() Tags: [ai](), [bug](), [data-pipelines](), [jobs](), [microservices](), [monitoring](), [reliability]() ### AI overview Spotify describes how rapid change, content-processing weaknesses, capacity limits, and a scheduling bug contributed to delays in publishing episodes. It reports adding end-to-end monitoring, fixing the scheduler, lowering batch-job priority, and increasing capacity. ### Source excerpt Quality and reliability have always been a point of pride for Spotify. We run an extraordinarily complex... The post AI Changed How Spotify Builds. What We Learned (and Fixed) About Quality at Higher Velocity appeared first on Spotify Engineering. ## Build an AI-powered product tagging system with Amazon SageMaker serverless model customization DevFeed: [Build an AI-powered product tagging system with Amazon SageMaker serverless model customization]() Original publisher: [Read original article]() Author: Linpo Guo Published: 2026-09-15T16:11:36Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Artificial Intelligence](), [Amazon SageMaker AI](), [Fine-tuning](), [rlvr](), [grpo](), [Inference](), [model-deployment](), [Reinforcement learning](), [SDK](), [Serverless]() Tags: [ai](), [amazon-sagemaker](), [amazon-sagemaker-ai](), [aws](), [compute](), [cost](), [customization](), [expert-400](), [fine-tuning](), [inference](), [jobs](), [reinforcement-learning](), [rlvr](), [sdk](), [technical-how-to]() ### AI overview This walkthrough shows how to build a product tagging system by customizing Qwen3-8B with supervised fine-tuning and reinforcement learning with verifiable rewards on Amazon SageMaker serverless model customization. It then deploys the optimized model for asynchronous inference to enrich retail catalogs. ### Source excerpt Manually tagging thousands of catalog products is slow and inconsistent. This walkthrough shows how to customize Qwen3-8B with supervised fine-tuning (SFT) and reinforcement learning with verifiable rewards (RLVR) on Amazon SageMaker serverless model customization, then deploy it for asynchronous inference to build a cost-efficient product tagging system. ## 47,000 job listings reveal the engineering roles that AI is creating DevFeed: [47,000 job listings reveal the engineering roles that AI is creating]() Original publisher: [Read original article]() Author: Jennifer Riggins Published: 2026-09-10T13:09:28Z Content type: news Language: en Sources: [The New Stack]() Topics: [Machine Learning & Artificial Intelligence](), [AI Development](), [Machine learning]() Tags: [ai](), [ai-engineering](), [ai-strategy](), [andela](), [engineering](), [jobs](), [machine-learning](), [post](), [skills](), [sponsor-andela](), [sponsored](), [sponsored-post](), [tech-careers](), [work]() ### AI overview Andela's analysis of 47,000 Fortune 500 engineering job postings identifies emerging AI-related roles formed by combining established skill sets. The article argues that organizations should use AI to delegate suitable work while retaining human expertise and specialization. ### Source excerpt Every major transformation in tech has led to roles merging, then new ones emerging. Friction between developers and operations drove The post 47,000 job listings reveal the engineering roles that AI is creating appeared first on The New Stack. ## Async GRPO with LoRA across HF Jobs: a bucket, a proxy, and no NCCL DevFeed: [Async GRPO with LoRA across HF Jobs: a bucket, a proxy, and no NCCL]() Original publisher: [Read original article]() Author: Amine Dirhoussi; Quentin Gallouédec; Kashif Rasul; Sergio Paniego Published: 2026-09-10T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [lora](), [grpo](), [jobs](), [vllm](), [NCCL](), [async](), [hugging face]() Tags: [async](), [grpo](), [hub](), [hugging-face](), [jobs](), [llm](), [lora](), [nccl](), [open-source](), [rl](), [storage](), [trl](), [vllm]() ### AI overview This article describes asynchronous GRPO training with a LoRA adapter across separate Hugging Face Jobs. The adapter is synchronized to vLLM replicas through a shared Storage Bucket, while a proxy handles authentication, rollout routing, and adapter-load broadcasts. Five runs reduced the time for 500 steps from 3 hours 27 minutes to 53 minutes. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Distributed tracing for CI pipelines without touching a single workflow file DevFeed: [Distributed tracing for CI pipelines without touching a single workflow file]() Original publisher: [Read original article]() Author: George Sims, downtherabbithole.dev Published: 2026-09-08T11:00:00Z Content type: article Language: en Sources: [Cloud Native Computing Foundation]() Topics: [ci]() Tags: [blog](), [ci](), [github](), [github-actions](), [instrumentation](), [jobs](), [opentelemetry](), [tracing](), [workflow](), [workflows]() ### AI overview The article describes collecting GitHub Actions events at the organization level and converting them into OpenTelemetry spans to trace CI workflows without editing individual workflow files. ### Source excerpt You've probably felt this one: GitHub Actions usage creeps up across your org, and your actual visibility into it doesn't keep pace. Which workflows are slow? Which are flaky? How long are jobs sitting queued for... ## Training a coding model to paint watercolours with TRL and OpenEnv DevFeed: [Training a coding model to paint watercolours with TRL and OpenEnv]() Original publisher: [Read original article]() Author: Sergio Paniego Published: 2026-09-03T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [openenv](), [coding](), [Language models](), [hugging face](), [AI Development](), [jobs](), [dataset]() Tags: [ai-art](), [coding](), [grpo](), [guide](), [hugging-face](), [javascript](), [jobs](), [open-source](), [openenv](), [rl](), [spaces](), [training](), [trl]() ### AI overview A tutorial describing an open reproduction of a reinforcement-learning pipeline that trains a coding model to create watercolor-like paintings by writing JavaScript with p5.brush. It uses TRL and OpenEnv, with datasets, environments, training scripts, models, and other artifacts published on Hugging Face. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## How Hugging Face Inference Endpoints, Jobs, and Buckets Power Search on Papers with Code DevFeed: [How Hugging Face Inference Endpoints, Jobs, and Buckets Power Search on Papers with Code]() Original publisher: [Read original article]() Author: Niels Rogge Published: 2026-08-21T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [AI search](), [inference-endpoints](), [jobs](), [hugging face](), [Embeddings](), [PostgreSQL](), [Retrieval Augmented Generation (RAG)]() Tags: [database](), [embeddings](), [hugging-face](), [inference](), [inference-endpoints](), [jobs](), [latency](), [rag](), [research](), [search](), [storage]() ### AI overview This article explains how Papers with Code uses hybrid search to find research papers through exact keyword matching and semantic vector search. The production system combines PostgreSQL full-text search, pgvector embeddings, reciprocal rank fusion, and Hugging Face Jobs, Storage Buckets, and Inference Endpoints. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Meta Offers Equity Retainers as Engineers Consider Leaving After Layoffs and Reassignments DevFeed: [Meta Offers Equity Retainers as Engineers Consider Leaving After Layoffs and Reassignments]() Original publisher: [Read original article]() Author: Gergely Orosz Published: 2026-08-20T18:08:45Z Content type: opinion Language: en Sources: [The Pragmatic Engineer]() Topics: [Meta](), [Product Management](), [Software]() Tags: [anthropic](), [engineering](), [equity](), [google](), [jobs](), [leaving](), [meta](), [openai](), [product-management]() ### AI overview The article argues that Meta's layoffs and forced reassignments prompted engineers to seek other jobs. It reports that Meta began offering large equity retainers to some departing senior engineers, including those considering moves to Google, Anthropic, and OpenAI. ### Source excerpt In what was predictable: Meta's layoffs and forced reassignments pushed engineers not impacted by either to look for a new job. Meta is now offering large equity retainers, and it doesn't seem working ## Same Cluster, 33 Points More Utilization: What Changed Was the Order DevFeed: [Same Cluster, 33 Points More Utilization: What Changed Was the Order]() Original publisher: [Read original article]() Author: Gabriel Pimenta de Freitas Cardoso; Breno de Almeida Beleza; Francisco de Almeida Rocha Alves; Bruno Duarte Published: 2026-08-17T19:46:21Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [GPU](), [benchmarking](), [Benchmark](), [jobs](), [Inference](), [model-deployment]() Tags: [batch](), [benchmark](), [comparison](), [gpu](), [inference](), [jobs](), [real-time](), [training]() ### AI overview The article describes a constraint-aware GPU allocator and compares it with a FIFO scheduler across seven benchmark scenarios. On identical hardware and workloads, the allocator increased GPU utilization by up to 33 percentage points and priority-weighted output by up to 105%. It explains how training, batch inference, quantization, and real-time inference impose different scheduling constraints, especially under contention. ### Source excerpt We built a constraint-aware GPU allocator and benchmarked it against a FIFO scheduler across seven benchmark scenarios. On identical hardware, running identical workloads, GPU utilization rose by as much as 33 percentage points, and priority-weighted output rose in every one of them, by as much as 105%. Nothing about the hardware changed. What changed was the order in which allocation decisions get made. One note on measurement before the numbers start. ## OpenAI joins PORTS-Pike project DevFeed: [OpenAI joins PORTS-Pike project]() Original publisher: [Read original article]() Published: 2026-08-17T05:00:00Z Content type: news Language: en Sources: [OpenAI News]() Topics: [datacenter](), [OpenAI](), [Nvidia](), [jobs]() Tags: [data-center](), [energy](), [global-affairs](), [infrastructure](), [jobs](), [local](), [nvidia](), [openai](), [partnership]() ### AI overview OpenAI has agreed to secure approximately 8 gigawatts-IT at the PORTS-Pike Technology Campus in Ohio in partnership with SB Energy, NVIDIA, and the U.S. Department of Energy. The project is expected to create construction and long-term operating jobs, fund community priorities, and support local infrastructure. Its data center will pay its energy and infrastructure costs and use closed-loop, air-cooled cooling systems to reduce ongoing water demand. ### Source excerpt OpenAI joins PORTS-Pike project, expanding community investment and supporting thousands of Southern Ohio jobs ## Adobe Firefly: Simplified observability with Amazon Managed Prometheus DevFeed: [Adobe Firefly: Simplified observability with Amazon Managed Prometheus]() Original publisher: [Read original article]() Author: Dev Arora Published: 2026-08-13T00:14:19Z Content type: article Language: en Sources: [AWS Architecture Blog]() Topics: [observability](), [GPU](), [Amazon EKS](), [Amazon Web Services](), [Monitoring](), [telemetry](), [migration](), [Training AI Models]() Tags: [adobe](), [amazon](), [amazon-eks](), [amazon-elastic-kubernetes-service](), [amazon-managed-service-for-prometheus](), [amazon-web-services-aws](), [aws](), [compute](), [cpu](), [customer-solutions](), [data](), [deployment](), [efficiency](), [gpu](), [infrastructure](), [jobs](), [kubernetes](), [metrics](), [observability](), [performance](), [prometheus]() ### AI overview Adobe Firefly migrated critical GPU infrastructure metrics from self-managed Prometheus to Amazon Managed Service for Prometheus. The article describes the observability challenges of large-scale model training on Amazon EKS, including high-cardinality GPU, compute, memory, and network telemetry, and reports 28x faster GPU metric queries with improved reliability and operational efficiency. ### Source excerpt Learn how Adobe Firefly achieved 28x faster GPU metric queries by migrating from self-managed Prometheus to Amazon Managed Service for Prometheus, with improvements in query performance, infrastructure reliability, and operational efficiency. ## Rerun Only the Jobs That Failed DevFeed: [Rerun Only the Jobs That Failed]() Original publisher: [Read original article]() Author: Pete Miloravac Published: 2026-08-12T10:31:39Z Content type: release Language: en Sources: [Semaphore Engineering]() Topics: [ci](), [jobs](), [Development]() Tags: [2026](), [ci](), [cost](), [flag](), [jobs](), [pipeline](), [product-news](), [rebuilds](), [run](), [workflow]() ### AI overview Semaphore now reruns only failed jobs in a pipeline, carrying over successful jobs and preserving the workflow topology. This reduces rerun time and billable usage, while a YAML flag preserves the previous full-block rebuild behavior. ### Source excerpt Your pipeline fails on one job, and until now you had to rebuild the entire block to recover. Not anymore. Semaphore now reruns only the jobs that actually failed, so you get feedback faster and pay less to get it. What Shipped: Job Rerun When a pipeline failed, the old behavior rebuilt every job inside [...] The post Rerun Only the Jobs That Failed appeared first on Semaphore. ## Fair by design: orchestrating background jobs in Ruby DevFeed: [Fair by design: orchestrating background jobs in Ruby]() Original publisher: [Read original article]() Author: Travis Turner (richardturner@evilmartians.com) Published: 2026-08-11T00:00:00Z Content type: tutorial Language: en Sources: [Evil Martians]() Topics: [Ruby](), [jobs](), [Latency](), [autoscaling]() Tags: [autoscaling](), [how-to](), [jobs](), [latency](), [performance](), [rails](), [redis](), [ruby](), [sidekiq]() ### AI overview This tutorial examines fairness in Ruby background-job processing. It explains how queue latency affects quality of service, why adding workers or autoscaling may be limited by shared resources and operational cost, and introduces background-job prioritization as a way to address bottlenecks. ### Source excerpt Are you treating your users fairly? They could be stuck in the queue while a greedy user monopolizes resources. And you might not even know it! In this post, you'll see if it's time for you to take background job prioritization seriously, and how to make it fair for all users. ## The Human Context Advantage: Takeaways from the Ai4 Keynote with Andrew Ng, Geoffrey Hinton, and Fei-Fei Li DevFeed: [The Human Context Advantage: Takeaways from the Ai4 Keynote with Andrew Ng, Geoffrey Hinton, and Fei-Fei Li]() Original publisher: [Read original article]() Author: Bonnie Chase Published: 2026-08-10T00:00:00Z Content type: opinion Language: en Sources: [Vespa Blog]() Topics: [Artificial Intelligence](), [AI Strategy](), [systems]() Tags: [agents](), [ai](), [business](), [developers](), [education](), [future-of-work](), [genai](), [jobs](), [keynote](), [rag](), [work]() ### AI overview The article reflects on an Ai4 keynote featuring Andrew Ng, Geoffrey Hinton, and Fei-Fei Li. It argues that humans retain a context advantage because they understand organizational goals, customers, relationships, constraints, and unwritten rules that current AI systems lack. The article suggests that enterprise AI success will depend on delivering relevant context at the right time, while AI changes jobs by automating tasks and broadening developers' responsibilities rather than simply eliminating work. ### Source excerpt From jobs and education to open models and AI infrastructure, the AI4 keynote made one thing clear: competitive advantage won't come from AI alone, but from connecting it to the right context. ## How HeyGen runs millions of AI workflows on Temporal DevFeed: [How HeyGen runs millions of AI workflows on Temporal]() Original publisher: [Read original article]() Author: Jiajun Zhao Published: 2026-08-06T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Orchestration](), [jobs](), [GPU](), [Cloud]() Tags: [cloud](), [gpu](), [jobs](), [orchestration](), [scale](), [temporal-voices]() ### AI overview HeyGen replaced an internal job-queue system with Temporal to coordinate video-generation workflows across many services, task queues, worker deployments, and a multi-cloud GPU fleet. The article explains the operational problems in the former system and the platform capabilities used to scale workflow execution. ### Source excerpt HeyGen replaced a brittle job-queue system with Temporal, coordinating millions of daily workflow runs across a multi-cloud GPU fleet. ## Internal Developer Portal: How Do I Get Started? DevFeed: [Internal Developer Portal: How Do I Get Started?]() Original publisher: [Read original article]() Author: Sooraj Shah Published: 2026-07-30T10:23:42Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [internal developer portal](), [Developer experience](), [Platform Engineering](), [dev-tools](), [Microservice](), [Cloud Native Ecosystem](), [.NET MAUI]() Tags: [catalog](), [cloud-native](), [dev-tools](), [developer-experience](), [developer-portal](), [internal-developer-portal](), [jobs](), [microservices](), [onboarding](), [platform](), [platform-engineering](), [user-stories]() ### AI overview This article explains how platform engineering teams can start an internal developer portal by building a focused minimum viable product. It recommends reducing developer complexity, grounding the portal in real use cases and user stories, gathering stakeholder feedback, and improving the portal iteratively. ### Source excerpt You're using more dev tools that solve different problems, and now your breaking the developer experience. Learn the first steps to get started. ## Project-scoped Tokens DevFeed: [Project-scoped Tokens]() Original publisher: [Read original article]() Author: Bel Curcio Published: 2026-07-30T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Vercel](), [API]() Tags: [api](), [jobs](), [token](), [tokens](), [tools](), [vercel](), [workflows]() ### AI overview Vercel now supports access tokens scoped to a single project. These tokens can read and write resources only within the selected project; requests targeting other projects or user- or team-level resources are denied. ### Source excerpt You can now create Vercel Access Tokens that are limited to a project to authenticate and use the Vercel API. A project-scoped token can only read and write resources belonging to a project that the token is scoped to. Requests to any other project, a user-level resource, or a team-level resource will be denied. This ensures jobs, tools, or workflows only ever access the projects they are scoped to. Creating a project-scoped token Navigate to the Account Tokens page, found under the Settings area of your Account. Enter a descriptive token name Open the Scope dropdown and select the team that owns the project, then click the team to drill into its list of projects Select the project you want the token to be limited to Choose an expiration and click Create Make a note of the token created as it will not be shown again Read more ## Batch Jobs for SparkClient: Submitting and Managing Spark Workloads from Python DevFeed: [Batch Jobs for SparkClient: Submitting and Managing Spark Workloads from Python]() Original publisher: [Read original article]() Author: Sameer Yadav Published: 2026-07-25T05:00:00Z Content type: tutorial Language: en Sources: [Kubeflow]() Topics: [Apache Spark](), [SDKs](), [Python](), [Kubernetes]() Tags: [batch](), [cleanup](), [gsoc](), [jobs](), [kubernetes](), [logs](), [pipelines](), [python](), [scheduled](), [sdk](), [spark]() ### AI overview This tutorial explains how the Kubeflow SDK's SparkClient supports submitting and managing batch Spark workloads on Kubernetes from Python. It covers script- and function-based jobs, lifecycle operations, log retrieval, cleanup, and the implementation's current boundaries. ### Source excerpt How the SparkClient SDK's new batch job APIs work under the hood -- submit_job(), FileJob/FuncJob, the lifecycle APIs, and log retrieval. ## Does a PhD Pay Off? DevFeed: [Does a PhD Pay Off?]() Original publisher: [Read original article]() Author: Daniel Lemire Published: 2026-07-24T20:13:57Z Content type: opinion Language: en Sources: [Daniel Lemire]() Topics: [Learning](), [Machine learning]() Tags: [career](), [career-progression](), [cost](), [experience](), [industry](), [jobs](), [learning](), [machine-learning](), [tech-industry](), [university](), [work]() ### AI overview This opinion examines whether pursuing a PhD pays off financially and professionally. It argues that the historical earnings advantage is concentrated among people who become professors, while delayed earnings and career progression create substantial opportunity costs. It notes that machine learning may be an exception because PhDs are often expected for some industry roles. ### Source excerpt Every week, I discuss with people who want to get a PhD. For years, I have been advising people not to pursue a PhD. It may come as a surprise to some. You would expect people with a PhD to earn more money. Individuals who complete doctorates tend to have higher cognitive abilities and greater ... Continue reading Does a PhD Pay Off? ## Building AI infrastructure with the Effingham County community DevFeed: [Building AI infrastructure with the Effingham County community]() Original publisher: [Read original article]() Published: 2026-07-22T13:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [AI Infrastructure](), [datacenter](), [OpenAI](), [codex]() Tags: [ai-infrastructure](), [codex](), [community](), [data-center](), [data-centers](), [global-affairs](), [infrastructure](), [jobs](), [openai]() ### AI overview OpenAI outlines Project Camellia, a planned datacenter in Effingham County, Georgia, including phased power delivery, a closed-loop water system, community benefits, local jobs, tax revenue, and Codex credits. ### Source excerpt OpenAI announces Project Camellia in Effingham County, Georgia, with commitments to responsible energy, community investment, jobs, and access to Codex. ## Millions of Batch Jobs Migrated in Four Weeks DevFeed: [Millions of Batch Jobs Migrated in Four Weeks]() Original publisher: [Read original article]() Author: Byte-Sized Design Published: 2026-07-13T17:15:37Z Content type: article Language: en Sources: [Byte-Sized Design]() Topics: [jobs]() Tags: [batch](), [jobs]() ### AI overview The article describes migrating millions of batch jobs in four weeks without users noticing. ### Source excerpt Millions of batch jobs, migrated in 4 weeks, and nobody noticed ## Profiling in PyTorch (Part 3): Attention is all you profile DevFeed: [Profiling in PyTorch (Part 3): Attention is all you profile]() Original publisher: [Read original article]() Author: Aritra Roy Gosthipaty; Sergio Paniego; Sayak Paul; Rémi Ouazan Reboul Published: 2026-07-10T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [AI, ML & Data Engineering](), [jobs](), [cpu]() Tags: [artificial-intelligence](), [cudnn](), [flash](), [gpu](), [jobs](), [kernel](), [profile](), [pytorch](), [torch](), [traces](), [xformers]() ### AI overview A PyTorch profiling tutorial examines naive attention, identifying its primitive operations and the CPU and GPU kernels they launch. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Run a vLLM Server on HF Jobs in One Command DevFeed: [Run a vLLM Server on HF Jobs in One Command]() Original publisher: [Read original article]() Author: Quentin Gallouédec Published: 2026-06-26T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [jobs](), [vllm](), [API](), [inference-endpoints](), [model-deployment](), [cURL](), [GPU](), [Python]() Tags: [api](), [gpu](), [guide](), [inference](), [inference-endpoints](), [infrastructure](), [jobs](), [json](), [model](), [models](), [python](), [vllm]() ### AI overview A practical guide to launching a vLLM model server on Hugging Face Jobs with a single command, querying it through the OpenAI-compatible API, authenticating requests with an HF token, managing costs, and scaling to larger multi-GPU models. It also contrasts ephemeral Jobs with managed Inference Endpoints. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Inspect Volcano workloads faster with Headlamp DevFeed: [Inspect Volcano workloads faster with Headlamp]() Original publisher: [Read original article]() Author: Mahmoud Magdy Published: 2026-06-25T20:00:00Z Content type: article Language: en Sources: [Kubernetes Blog]() Topics: [Kubernetes](), [jobs](), [High-Performance Computing](), [Machine learning](), [Web](), [Command-line interface]() Tags: [ai-ml](), [cli](), [high-performance-computing](), [jobs](), [kubernetes](), [quotas](), [ui](), [workflow]() ### AI overview This article introduces the Volcano plugin for Headlamp, a Kubernetes web UI. The plugin brings Volcano jobs, queues, PodGroups, Pods, and events into a unified interface, helping teams inspect batch workloads, scheduling behavior, quotas, priorities, and gang scheduling for Kubernetes, AI/ML, and high-performance computing workloads. ### Source excerpt Volcano is a cloud native batch scheduler for Kubernetes, built for high-performance computing, AI/ML, and other batch workloads. Headlamp is an extensible Kubernetes web UI. With its plugin system, Headlamp can surface APIs and workflows beyond the built-in Kubernetes resources. The Volcano plugin brings core Volcano resources into Headlamp so you can inspect workload state, queue behavior, and gang scheduling details in one place. Kubernetes was originally designed around long-running services, where applications are expected to start and remain available over time. Batch, AI/ML, and HPC workloads often behave differently: jobs arrive dynamically, compete for limited resources, and may need multiple workers to start together before useful work can begin. Volcano extends Kubernetes with concepts such as queues, priorities, quotas, and gang scheduling. Instead of treating every Pod independently, Volcano schedules workloads with awareness of the job as a whole and the resources it needs to make progress. To make these workloads easier to operate and troubleshoot, the Volcano plugin brings that scheduling context directly into Headlamp. Watch this short walkthrough to see the Volcano plugin in Headlamp: Visual context helps teams understand Volcano jobs, queues, and PodGroups faster Working with Volcano often means moving across several related resources while trying to understand a batch workload. You might start with a Job, then look at the related PodGroup, inspect the Pods behind it, check the Queue, and finally return to the Job again. All of that is possible with CLI tools like kubectl and the Volcano CLI, but it can become fragmented very quickly. The Volcano plugin for Headlamp makes that workflow easier by bringing the key resources together in a single UI. Instead of reconstructing relationships manually, you can move directly between Jobs, Queues, PodGroups, Pods, and events from the same interface. Volcano introduces its own resources on top of core Kuber [Next page]()