Staff+ Infrastructure Engineer, Cluster Infrastructure

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Anthropic's Infrastructure organization is foundational to our mission of developing AI systems that are reliable, interpretable, and steerable. The systems we build determine how quickly we can train new models, how reliably we can run safety experiments, and how effectively we can scale Claude to millions of users — demonstrating that safe, reliable infrastructure and frontier capabilities can go hand in hand. Cluster Infra owns the full lifecycle of compute clusters at Anthropic. We build agent-driven automation for cluster provisioning and lifecycle management across all major cloud providers and our own datacenters. Our systems stand up clusters that are interconnected with high bandwidth, secure-by-default, and able to automatically drain and recover in response to failure. As a Staff engineer on this team, you'll set the technical direction for how Anthropic brings compute online - at a moment when the scale of that compute is growing faster than at almost any company in the world. Key responsibilities Own the technical strategy and roadmap for agent-driven cluster lifecycle management - provisioning, updates and decommissioning Partner across teams to ensure new compute capacity is ingested on time Align with partner teams on physical build-out and leverage cloud solutions to deliver high-bandwidth inter-cluster connectivity Collaborate with security owners to ensure clusters are provisioned secure-by-default Define and drive strategy on cluster scalability, homogeneity and fault tolerance Work closely with cloud providers and internal research, inference and product teams to shape long-term compute, data, and i

We are looking for an AI Infra engineer to join our growing team. We work with Kubernetes, Slurm, Python, C++, PyTorch, and primarily on AWS. As an AI Infrastructure Engineer, you will be partnering closely with our Inference and Research teams to build, deploy, and optimize our large-scale AI training and inference clusters Responsibilities Design, deploy, and maintain scalable Kubernetes clusters for AI model inference and training workloads Manage and optimize Slurm-based HPC environments for distributed training of large language models Develop robust APIs and orchestration systems for both training pipelines and inference services Implement resource scheduling and job management systems across heterogeneous compute environments Benchmark system performance, diagnose bottlenecks, and implement improvements across both training and inference infrastructure Build monitoring, alerting, and observability solutions tailored to ML workloads running on Kubernetes and Slurm Respond swiftly to system outages and collaborate across teams to maintain high uptime for critical training runs and inference services Optimize cluster utilization and implement autoscaling strategies for dynamic workload demands Qualifications Strong expertise in Kubernetes administration, including custom resource definitions, operators, and cluster management Hands-on experience with Slurm workload management, including job scheduling, resource allocation, and cluster optimization Experience with deploying and managing distributed training systems at scale Deep understanding of container orchestration and distributed systems architecture High level familiarity with LLM architecture and training processes (Multi-Head Attention, Multi/Grouped-Query, distributed training strategies) Experience managing GPU clusters and optimizing compute resource utilization Required Skills Expert-level Kubernetes administration and YAML configuration management Proficiency with Slurm job scheduling, resource manag

We are looking for an AI Infra engineer to join our growing team. We work with Kubernetes, Slurm, Python, C++, PyTorch, and primarily on AWS. As an AI Infrastructure Engineer, you will be partnering closely with our Inference and Research teams to build, deploy, and optimize our large-scale AI training and inference clusters. Responsibilities Design, deploy, and maintain scalable Kubernetes clusters for AI model inference and training workloads Manage and optimize Slurm-based HPC environments for distributed training of large language models Develop robust APIs and orchestration systems for both training pipelines and inference services Implement resource scheduling and job management systems across heterogeneous compute environments Benchmark system performance, diagnose bottlenecks, and implement improvements across both training and inference infrastructure Build monitoring, alerting, and observability solutions tailored to ML workloads running on Kubernetes and Slurm Respond swiftly to system outages and collaborate across teams to maintain high uptime for critical training runs and inference services Optimize cluster utilization and implement autoscaling strategies for dynamic workload demands Qualifications Strong expertise in Kubernetes administration, including custom resource definitions, operators, and cluster management Hands-on experience with Slurm workload management, including job scheduling, resource allocation, and cluster optimization Experience with deploying and managing distributed training systems at scale Deep understanding of container orchestration and distributed systems architecture High level familiarity with LLM architecture and training processes (Multi-Head Attention, Multi/Grouped-Query, distributed training strategies) Experience managing GPU clusters and optimizing compute resource utilization Required Skills Expert-level Kubernetes administration and YAML configuration management Proficiency with Slurm job scheduling, resource mana

Who are we? FalconX is a pioneering team of operators, investors, and builders committed to revolutionizing institutional access to the crypto markets. Operating at the intersection of traditional finance and cutting-edge technology, FalconX addresses the industry's foremost challenges: Navigating the digital asset market can be complex and fragmented, with limited products and services that support trading strategies, structures, and liquidity found in conventional financial markets. As a comprehensive solution for all digital asset strategies from start to scale, FalconX operates as the connective tissue empowering clients with seamless navigation through the ever- evolving cryptocurrency landscape. Responsibilities Low-Latency Architecture: Design, provision, and maintain ultra-low latency AWS and hybrid network topologies, including VPCs, exchange gateways, and regional deployment expansions. Performance Tuning: Optimize Linux OS kernels and compute performance using CPU isolation (isolcpus), AWS Cluster Placement Groups, and enhanced networking (ENA/ENI). Infrastructure as Code: Drive infrastructure automation, provisioning, and configuration management using Terraform and Ansible. Platform Scaling: Architect infrastructure to support expanding Exchange Market Making (EMM), Project Market Making (PMM), and TradFi exchange convergences. Observability & Reliability: Build telemetry and monitoring pipelines using Grafana Cloud and Alloy to ensure continuous visibility into platform performance. Technical Leadership: Guide the technical roadmap, mentor junior engineers, and act as an escalation lead for critical system incidents. Key Qualifications 5+ years of experience in Infrastructure Engineering, Systems Engineering, or SRE, with a focus on low-latency electronic trading environments . Deep Linux Internals Expertise: Advanced experience with kernel-level performance tuning, memory management, and network I/O optimization (isolcpus, network profiling, ring

P-1346 At Databricks, we are passionate about enabling data teams to solve the world's toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business. Founded by engineers — and customer obsessed — we leap at every opportunity to solve technical challenges, from designing next-gen UI/UX for interfacing with data to scaling our services and infrastructure across millions of virtual machines. Databricks Mosaic AI offers a unique data-centric approach to building enterprise-quality, Machine Learning and Generative AI solutions, enabling organizations to securely and cost-effectively own and host ML and Generative AI models, augmented or trained with their enterprise data. And we're only getting started in Bengaluru , India - and currently in the process of setting up 14 new teams from scratch ! As a Staff Software Engineer at Databricks India, you can get to work across : - Backend (Infrastructure) The impact you'll have: Our Infrastructure Backend teams span many domains across our essential service platforms. For instance, you might work on challenges such as: Problems that span from product to infrastructure including: distributed systems, at-scale service architecture and monitoring, workflow orchestration, and developer experience. Deliver reliable and high performance services and client libraries for storing and accessing humongous amount of data on cloud storage backends, e.g., AWS S3, Azure Blob Store. Build reliable, scalable services, e.g. Scala, Kubernetes, and data pipelines, e.g. Apache Spark™, Databricks, to power the pricing infrastructure that serves millions of cluster-hours per day and develop product features that empower customers to easily view and control platform usage. What we look for

Salary Range: PLN 350,700- 474,400 Subject to alignment to the responsibilities and duties of the role. Location: Gdańsk - Candidates must be local due to hybrid working policy About the job Make AI accelerator hardware feel native inside Kubernetes clusters. As a Staff Cloud Software Engineer, you will build Go services that connect accelerator hardware with Kubernetes. You will own software components that turn complex compute capability into a usable platform experience. Your work will shape how AI workloads discover, access and scale across cluster infrastructure. That means designing production services where reliability, performance and observability are part of the architecture. You will build CRDs, operators and Kubernetes components for ML and platform teams. You will partner with hardware, cloud and ML engineers on design choices and roadmap priorities. This is a senior IC role with room to set direction. You will mentor engineers, raise code quality and help define how Graphcore delivers cloud-native AI compute. The team and culture The Workload Management team sits between Kubernetes, cloud platforms and accelerator hardware. Work moves from architecture to implementation, review and production readiness without losing system context. You will be trusted to own technical areas, challenge assumptions and move important work forward. The pace is driven by clear priorities, fast feedback and strong engineering judgement. Decisions are made through evidence, prototypes and direct technical discussion. Engineers are expected to speak up early, share trade-offs and help the team learn quickly. What we’re looking for Strong Go engineering experience, with significant experience delivering complex production software Deep Kubernetes architecture knowledge, including CRDs, operators and cloud-native integration patterns Solid Linux understanding, with the ability to debug issues across distributed systems Experience leading technical initiatives and mentoring eng