Software Engineer, GPU Infrastructure- ChatGPT Engineering

About the Role REMOTE IN INDIA We're looking for a software engineer to build the Kubernetes-native control plane that provisions and runs our GPU inference fleet. You'll design a manifest-driven API where the inference team declares what they need, whether that's a cluster, a model deployment, or a capacity change, and our controllers handle the reconciliation, provider/runtime selection, and lifecycle management underneath, so the inference team never has to know or care which specific serving stack, scheduler, or hardware pool is doing the work. You'll also build the systems that keep the fleet efficient, not just running, including defragmentation and rebalancing logic that consolidates scattered workloads back into contiguous capacity, and scheduling/bin-packing improvements that push GPU utilization up without hurting latency. The core value we're after is decoupling the people building on top of the platform from the operational and runtime complexity underneath, while squeezing more usable capacity out of the same hardware. You'll build the controllers, reconciliation loops, and self-service surface (API/CLI, not tickets) that make that decoupling real, plus the event-driven health, remediation, and utilization systems that keep it running and efficient without a human in the loop. Strong candidates have hands-on experience with Kubernetes controller/CRD patterns, have built or operated a platform API that abstracts multiple backends behind one interface, understand GPU scheduling and capacity efficiency (fragmentation, bin-packing, right-sizing), and think about GPU infrastructure as software to be engineered. A product mindset - you've built internal platforms or APIs consumed by other engineering teams and care about the developer experience of what you ship. You build it, you own it. You are not only responsible for delivering the software but also for operating and supporting it in production. Responsibilities Build the provisioning state machine

About the Role Together AI runs one of the largest GPU fleets in the world. The Infra Agent Systems team builds the software systems that power and automate that infrastructure. We develop production AI agents that diagnose hardware failures, investigate incidents, correlate signals across the fleet, and automate operational workflows. Alongside these agents, we build the platform they run on, including knowledge graphs, retrieval systems, orchestration frameworks, and developer tooling. You’ll work across two areas: Infrastructure Agent Systems — Build production AI agents that help operate our GPU fleet by diagnosing failures, investigating incidents, gathering evidence from live systems, and assisting with remediation. These agents are used every day by our infrastructure and datacenter teams through APIs, CLI, dashboards, and Slack. Core Agent Platform — Build the platform that powers these agents, including knowledge graphs, search and retrieval, orchestration, evaluation, and the tooling that enables agents to reason, act, and continuously improve. We’re working on something that hasn’t really been done before: building knowledge graphs and self-improving AI agents that understand, operate, and continuously improve large-scale AI infrastructure. This is an opportunity to work at the intersection of AI agents, distributed systems, infrastructure, and automation , solving challenging engineering problems with real production impact. There’s an enormous amount to build, learn, and shape as we define the future of autonomous infrastructure. responsible for delivering the software but also for operating and supporting it in production. Why this Role You’ll work on two hard problems at the same time: making AI agents trustworthy enough to operate production infrastructure, and building the knowledge, retrieval, and distributed systems that make those agents effective. You’ll have the opportunity to build foundational systems from the ground up, work on infrastructur

Join the engineering teams that bring OpenAI’s ideas safely to the world!! The Applied Engineering team works across research, engineering, product, and design to bring OpenAI’s technology to consumers and businesses. We seek to learn from deployment and distribute the benefits of AI, while ensuring that this powerful tool is used responsibly and safely. Safety is more important to us than unfettered growth. About the Role As OpenAI continues to grow, we are looking for experienced, problem-solving engineers to ensure our systems scale. Our success depends on our ability to quickly iterate on products while also ensuring that they are performant and reliable. You will work in a deeply iterative, collaborative, fast-paced environment to bring our technology to millions of users around the world, and ensure it’s delivered with safety and reliability in mind. Successful candidates will play a crucial role in ensuring the reliability, scalability, and performance of our systems as we continue to expand. As a reliability expert, you will be at the forefront of maintaining and enhancing the stability, scalability, and performance of our rapidly evolving infrastructure. You will work closely with cross-functional teams, including software engineers, product managers, and data scientists, to build and maintain resilient systems that can handle our growing user base and workload. In this role, you will: Design and implement solutions to ensure the scalability of our infrastructure to meet rapidly increasing demands. Build and maintain the load, chaos and synthetic testing software leveraged by development teams to make the systems they design and operate more reliable. Build and maintain automation tools to streamline repetitive tasks and improve system reliability. Build and maintain the platform for CPU/storage, GPU, and network lifecycle management to drive efficiency, accountability and support dynamic optimization of our resources. Implement fault-tolerant and resilient

For over 20 years, Smartsheet has empowered teams to manage work seamlessly and scale solutions smarter. Now, in our most ambitious chapter yet, we are uniting human teams with AI agents. By orchestrating the work agents do best, automating manual tasks and uncovering insights at scale, we create the space for people to focus on what truly matters: judgment, creativity, and big thinking. That is magic at work, and it’s what we show up for every day. Job Description/ Responsibilities: Designing, developing and maintaining stable and reliable AI/ML Ops platforms / pipelines Minimum experience of 4-6 Years required in AI ML Ops Model Deployment: Package and deploy AI/ML services to production, ensuring they are reproducible and interpretable CI/CD Pipeline Development: Design and implement automated CI/CD (Continuous Integration/Continuous Deployment) pipelines to accelerate model deployment using tools Infrastructure Management: Provision and optimize infrastructure for training and serving, utilizing Docker, Kubernetes, or serverless platforms Monitoring & Observability : Implement post-deployment monitoring for model performance, data drift, and latency using tools. Experience in Monte Carlo is preferable Automation: Automate retraining and data pipeline workflows to ensure models stay accurate over time. Manage the deployment of foundation models, fine-tuning workflows, and Retrieval-Augmented Generation (RAG) stacks (Vector DBs, Knowledge Graph. Experience with AWS Bedrock is preferable Resource Optimization: Manage GPU/CPU utilization to minimize cloud costs while maintaining low-latency inference for users Collaboration: Work closely with data scientists, data engineers, and software engineers to bridge the gap between model development and production. Version Control & Governance: Manage versioning for data, code, and models using tools like MLflow. Security & Compliance: Implementing data security measures, ensuring compliance with data governance

About the Role At Together AI, you’ll build and operate one of the world’s largest GPU fleets used for frontier model training and inference. This isn’t a traditional infrastructure role—we’re looking for engineers who love building systems, automating everything, and solving problems at massive scale. If you enjoy writing software more than clicking dashboards, obsess over eliminating manual work, and want to build infrastructure that manages tens of thousands of GPUs autonomously, we’d love to talk. Responsibilities Design and build fleet automation systems that provision, validate, deploy, upgrade, repair, and retire GPU clusters with minimal human intervention. Build AI Infrastructure Agents that automate deployment, root-cause failures, incident triage, and autonomous remediation. Develop Fleet Intelligence platforms that continuously monitor hardware health, firmware, networking, storage, thermals, and workload performance to predict failures before they impact customers. Build software that maximizes GPU availability, utilization, performance, and reliability across thousands of accelerators. Create automated validation systems for GPUs, InfiniBand/RoCE fabrics, NVLink/NVSwitch, storage, and distributed AI workloads. Build internal platforms and developer tools that allow infrastructure to be managed through software—not manual operations. Continuously improve deployment velocity, reliability, and operational efficiency through automation. Partner closely with hardware, networking, platform, and AI teams to push the limits of AI infrastructure. Requirements 3+ years building distributed systems, infrastructure platforms, or large-scale backend software. Strong software engineering skills in Python, Go, or Rust . Experience building platforms, automation systems, or developer infrastructure. Experience with Linux, Kubernetes, Terraform, Ansible, or similar infrastructure technologies. Strong systems thinking with the ability to understand problems across hardw

For over 20 years, Smartsheet has empowered teams to manage work seamlessly and scale solutions smarter. Now, in our most ambitious chapter yet, we are uniting human teams with AI agents. By orchestrating the work agents do best, automating manual tasks and uncovering insights at scale, we create the space for people to focus on what truly matters: judgment, creativity, and big thinking. That is magic at work, and it’s what we show up for every day. Job Description/ Responsibilities: Designing, developing and maintaining stable and reliable AI/ML Ops platforms / pipelines Model Deployment: Package and deploy AI/ML services to production, ensuring they are reproducible and interpretable CI/CD Pipeline Development: Design and implement automated CI/CD (Continuous Integration/Continuous Deployment) pipelines to accelerate model deployment using tools Infrastructure Management: Provision and optimize infrastructure for training and serving, utilizing Docker, Kubernetes, or serverless platforms Monitoring & Observability : Implement post-deployment monitoring for model performance, data drift, and latency using tools. Experience in Monte Carlo is preferable Automation: Automate retraining and data pipeline workflows to ensure models stay accurate over time. Manage the deployment of foundation models, fine-tuning workflows, and Retrieval-Augmented Generation (RAG) stacks (Vector DBs, Knowledge Graph. Experience with AWS Bedrock is preferable Resource Optimization: Manage GPU/CPU utilization to minimize cloud costs while maintaining low-latency inference for users Collaboration: Work closely with data scientists, data engineers, and software engineers to bridge the gap between model development and production. Version Control & Governance: Manage versioning for data, code, and models using tools like MLflow. Security & Compliance: Implementing data security measures, ensuring compliance with data governance policies, and protecting sensitive data Technology Eva