Infrastructure Jobs

A World-Changing Company Palantir builds the world’s leading software for data-driven decisions and operations. By bringing the right data to the people who need it, our platforms empower our partners to develop lifesaving drugs, forecast supply chain disruptions, locate missing children, and more. The Role We’re looking for Forward Deployed Infrastructure Engineers who can help us build, operate, and maintain high-performance, scalable, and reliable services for Palantir platforms, products, and deployments. You'll get to use your creativity to develop novel solutions to evolving challenges and automate processes wherever possible, using whichever tools are best for the job including industry-leading LLM and AI technology! As a Forward Deployed Infrastructure Engineer, every day is different! You will be developing software and owning reliability and operations for software systems that are critical to solving our government’s greatest challenges. We strongly believe in engineering teams being responsible for the operations of their services in production. As such, you’ll work closely with both forward deployed teams and product teams to participate in sensible, scalable, systems design and share responsibility with them in diagnosing, resolving, and preventing production issues. Core Responsibilities Handle support and operations of Palantir software, including monitoring and alerting, configuration management, and upgrades Deploy new Palantir products across many production environments and perform migrations to the latest infrastructure types Debug, improve, and optimize Palantir’s services and infrastructure with a focus on long-term reliability and scalability Reduce manual operations and automate workflows, processes, and/or runbooks where possible Provide technical troubleshooting support for production issues, ensuring timely resolution and minimal impact on operations. Participate in a support on-call schedul

P-78 While candidates in the listed locations are encouraged for this role, candidates in other locations will be considered. 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. And we're only getting started. As a Senior Software Engineer on the Infrastructure teams, you will build scalable systems to power the Databricks platform, making it the de-facto platform for running Big Data and AI workloads. You will build and extend the Databricks infrastructure platform, which includes multi-cloud systems and services for managing thousands of Kubernetes clusters at scale, storing petabytes of data, highly scalable and distributed API gateways, rate limiting framework, network security and encryption, developer tools and infrastructure (we use Bazel), testing frameworks, and scalable CI/CD framework among many others. The impact you will have: Build and extend components of the core Databricks infrastructure Architect multi-cloud systems and abstractions to allow the Databricks product to run on top of existing Cloud providers Improve software development workflows for engineering and operational efficiency. Use our own data and AI platform (yes!) to analyze build and test logs and metrics to identify areas for improvement. Develop automated build, test, and release infrastructures. Set and uphold the standard for engineering processes to support high-quality engineering, including style and code checking, test harness

Job Details: Job Description: We are seeking an experienced Analog Design and Infrastructure DA Manager to lead the development, deployment, and governance of analog/mixed-signal design environments and CAD infrastructure. This role owns EDA tool ecosystems, PDK integration, compute infrastructure, design data governance, and tapeout manifest management to ensure high productivity, reproducibility, and audit readiness across silicon programs..The ideal candidate combines deep analog/mixed-signal design flow and e-test structure development expertise with strong infrastructure leadership and disciplined configuration/data management practices. Key Responsibilities-1. Analog Design Environment and Flow Management-Own and maintain analog and mixed-signal design flows using platforms such as Virtuoso ,Develop and maintain schematic, layout, verification, and extraction flows (LVS, DRC).Support simulation environments including HSPICE, Corner analysis.Drive automation and methodology improvements to reduce turnaround time and increase design robustness. 2. Infrastructure and Compute Management Oversee Linux-based DA infrastructure including compute farms, storage systems, and license servers (FlexLM). Manage LSF/grid environments and job scheduling systems. Ensure scalability, system monitoring, high availability, and performance optimization. Partner with IT on hardware lifecycle planning, cloud integration, and disaster recovery. Maintain secure, access-controlled design environments aligned with IP protection policies. 3. Design Data, Manifest and Configuration Management Design Data Governance Manage large-scale analog design libraries, hierarchical database structures, and technology libraries. Define backup, archival, and retention policies for tapeout-critical data. Implement data integrity validation and corruption prevention controls. Oversee distributed stor

Analyst / Senior Analyst - Energy & Infrastructure Advisory — London, GBR. Apply via Workday.

Principal Software Engineer - Core Infrastructure (Python/C++) — 2 Locations. Apply via Workday.

Principal Cloud Infrastructure Engineer (AWS) — RI - Woonsocket. Apply via Workday.

The Data Governance Foundation Lead Analyst is responsible for contributing to the directional strategy and assisting in creation and modification of Enterprise Data Governance Strategy, and/or Data Risk and Control Framework and Data Risk Taxonomy. The focus of this role may be on one or more of the following areas: assisting in identification, measurement and reporting, data policy adoption and compliance, data controls framework performance and issue management process, and regulatory and audit response and action tracking. This role requires someone who is dynamic, flexible, and can easily respond to quickly changing needs. The candidate must have the ability to handle ambiguity and complexity and be able to deliver on multiple responsibilities. Developed communication and diplomacy skills are required in order to guide, influence and convince others. Responsibilities: Partner with multiple teams to implement relevant Data Governance requirements and execute inline with policies and/or Data Risk and Control framework Oversight and analysis of data-related issues, tracking of ownership and target dates and associated metrics and reporting Identify and remediate data quality issues, including mapping inconsistencies, duplicate elements, invalid domains, and ineffective business rules Gather and synthesize metrics on existing Data Governance implementation and/or Data Risk and Controls principles, policies, practices and standards Produce recommendations for enterprise-wide guiding principles, policies, processes and practices Determine requirements for and prioritize development of new tools and requirements leveraging data and maintaining compliance with Data Governance requirements as a part of the Business as Usual (BAU) process Perform data analysis and build subject matter expertise of data and usage Maintain traceability between data sources, busine

Individuals in Operational Risk establish and manage operational risk policies, propose strategies, and governance processes designed to minimize losses from inadequate controls, fraud, and potential bankruptcy. This includes supervision over technology operational risk, cyber risk, and fraud risk. These individuals are also responsible for the development of tools and infrastructure that support fraud monitoring and prevention, using advanced analytical and statistical skills to identify policy gaps and recommend changes to current policies. This role is crucial to the company as it fortifies against potential operational risks, thereby protecting the company's financial stability and reputation, and contributing towards the overall business resilience and success. Responsibilities : Support the design and implementation of enterprise operational risk management procedures and methodologies, including effective application within the businesses. Independently assess operational risks in the business to ensure it is within the acceptable risk appetite Collaborate with the business to understand its risk profile, assess operational risk exposure, integrate risk mitigation controls, and monitor adherence. Review and challenge the effectiveness of risk mitigation strategies and control measures implemented by the 1st line of defense Provide regular reporting to senior management and stakeholders on the status of operational risks. Analyze data to identify trends or emerging risks and provide timely recommendations to mitigate risk exposure. Lead initiatives to improve operational risk management practices and enhance organizational resilience. Ensure operational risk management activities comply with internal policies, external regulations, and industry standards. Stay abreast of industry best practices and regulatory changes to assess its impact on operational risk man

NVIDIA’s EDA Infrastructure organization builds and operates the systems that support chip development. We are looking for an engineering manager to lead the team responsible for operational processes and platforms across incident management, maintenance, on-call, issue management, and customer-serving readiness. You will own the roadmap and delivery, from defining how teams work to building the tools they use. You will partner with infrastructure and service owners to improve reliability, reduce manual work, and ensure services are ready to support customers. Your team will use automation, AI, and lessons from operational events to drive improvements. What you’ll be doing: Lead a team and own the roadmap for operational processes and platforms, from requirements and delivery through adoption and results. Set technical direction, prioritize work, and guide execution across engineering and operational disciplines. Partner with infrastructure, product, and security teams to establish consistent practices for incident response, maintenance, on-call, issue management, and customer-serving readiness. Hire and develop engineers and technical leads, building a team with clear ownership and accountability. Align priorities across teams, communicate progress and risks, and provide technical leadership during major incidents. What we need to see: <span style="co

As a member of the Hardware Infrastructure EDA Compute team, you will optimize, scale, and support workload scheduling systems that directly impact design velocity and infrastructure efficiency. Success in this role requires both operational precision along with developing and supporting forward-looking resource management solutions that address evolving compute demands. Beyond day-to-day operations, the role drives improvements in observability, service reliability, and automation, ensuring the EDA compute environment remains resilient, measurable, and aligned with long-term engineering demands. What you'll be doing: Manage, scale, and optimize job scheduling systems (LSF, Slurm, etc.) in a large-scale, multi-site environment supporting EDA and other compute-intensive workloads Analyze scheduler and infrastructure performance data to identify systemic bottlenecks and drive measurable improvements in utilization, throughput, and turnaround time Lead problem solving across scheduler, OS, and workload layers, ensuring timely resolution of service-impacting issues Identify recurring operational challenges and implement targeted automation or process improvements to reduce manual effort and prevent repeat incidents Help define and track reliable metrics and SLOs for service performance and reliability, partnering with customers to ensure expectations are realistic and measurable Contribute to operational standards, documentation, and best practices to improve consistency across sites Partner directly with customer teams to clarify requirements, translate technical tradeoffs, and drive issues to closure What we need to see: Bachelor’s degree in Computer Science or related field, or equivalent experience Minimum 5&#43; years of experience operating and supporting large-scale Linux-based

As a member of the HW Infrastructure Storage Strategy team, you will be the bridge between storage infrastructure and the chip design (e.g. NVIDIA GPUs and CPUs) workloads that depend on it. You will focus on going hands-on to understand how storage behavior impacts EDA (Electronic Design Automation) and HPC workload performance in general — and what to do about it. You’ll evaluate next-generation storage technologies, design POCs grounded in real workload profiles, uncover gaps in observability, and drive strategic decisions like data placement during datacenter transitions. This is a rare role where you’ll work at the intersection of storage systems and chip design workflows at a scale few companies reach — petabytes of active data, millions of files per job, and thousands of concurrent users. You’ll develop NVIDIA’s storage strategy and have a direct, measurable impact on engineering velocity. What you'll be doing: Lead storage technology evaluations — design and execute POCs for platforms such as NetApp, VAST, and others, using real EDA workload profiles to benchmark performance, metadata behavior, and compatibility Analyze how storage configurations and architecture choices impact EDA workload runtime in production, and recommend tuning or architectural changes to reduce job execution time Identify gaps in storage telemetry and observability — define what metrics are needed and how to correlate storage behavior with jobs running on the environment Support datacenter lifecycle programs — expansions, technology refreshes, and site exits — by mapping workload placement, identifying required data, and minimizing data replication scope Define storage policies and best practices — data lifecycle, tiering strategies, snapshot/clone workflows (e.g., FlexClone for EDA workspace branching), and capacity planning recommendations Collaborate with IT, EDA tool teams, EDA operations, and

NVIDIA is looking for a Systems & Software Engineer interested in building and running reliable large scale infrastructure platform services. In this role you will create and support automation that manages infrastructure, as part of our organization running EDA platforms atop of NVIDIA hardware. What you’ll be doing: Design, build, deploy, and run infrastructure services & manage the software life cycle in scope to meet our business goals Build and maintain automation to eliminate manual toil and scale output without scaling effort Participate in the definition of our internal facing service level objectives and error budgets as part of our overall observability strategy Practice sustainable blameless incident prevention and incident response while being a member of an on-call rotation Consult with and provide consultation for peer teams on systems design best practices What we need to see: BS degree in Computer Science or a related technical field involving coding (e.g., physics or mathematics) or equivalent experience Experience coding in Python and/or Go. Note : you must be able to produce code without AI assistance during the interview process. Experience with infrastructure automation and distributed systems design Experience developing tools for running large scale private or public cloud systems in production In depth knowledge of Linux systems and Containers A track record showing a good balance between initiating your own projects, convincing others to collaborate with you and collaborating well on projects initiated by others. <

We are seeking a Linux Systems Engineer to join our Infrastructure Engineering team, dedicated to supporting high-performance EDA compute farms. You will be responsible for designing, deploying, and maintaining enterprise-grade Linux environments across RPM-based distributions, optimized for the high-throughput requirements of EDA workloads. You will drive automation initiatives, and build robust tooling to support a highly available and scalable infrastructure. What you'll be doing: Linux Infrastructure Engineering: Maintain, tune, and secure mission-critical Linux operating systems across production and non-production environments. End-to-End Automation: Lead infrastructure configuration management and continuous deployment workflows using Infrastructure as Code, e.g. Ansible and Terraform . Tooling Development: Write high-quality, maintainable code in Python, Bash, or Go to automate repetitive operational tasks and integrate infrastructure telemetry. Naming & Storage Services: Manage Linux Naming Services (LDAP/SSSD) and networked storage (NFS, Autofs) environments to support high-performance EDA workloads. What we need to see: Linux Internals: 8&#43; years of deep systems engineering experience working with RPM-based Linux distributions. Bachelor's degree or equivalent work experience. Infrastructure as Code: Strong track record of automating end-to-end multi-tier infrastructure configurations and managing centralized deployments. Experience with more than one configuration management system is a bonus. Virtualization and Container Architecture: Deep understanding of virtualization and container internals

Who are we? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Role Overview As a Member of Technical Staff in Post-Training Data Infrastructure, you will build and own the pipelines that produce the post-training data for our large language models (LLMs), from SFT and RL data to synthetic and human-annotated data. You will work closely with researchers and annotation teams so they can iterate on data as quickly as they iterate on models. Key Responsibilities Build and maintain data pipelines for SFT, preference, and RL post-training data. Own dataset quality, versioning, and reproducibility. Turn research data recipes into production-grade, reusable components. Scale model inference for data generation and model-based grading. Design data formats and schemas that keep post-training datasets consistent and efficient to train on. Collaborate with researchers and engineers to make sure data pipelines keep pace with the needs of cutting-edge language models. Qualifications Strong software engineering skills. Experience designing and building large-scale data pipelines for machine learning, ideally for LLM post-training. Track record of running distributed data processing and model inference at

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world. At NVIDIA, as a Principal Rack Scale Systems Infrastructure Engineer, you will build and guide the development of software systems. These systems support our upcoming rack-scale infrastructure products and services. This exceptional role sits where software meets hardware. You will work on control planes, state machines, orchestration systems, firmware, OS lifecycle, and networking fabrics. Your task is to compose infrastructure-as-a-service control plane software that converts complex rack-scale hardware into dependable, manageable, and programmable infrastructure for NVIDIA, partners, and leading cloud and enterprise clients globally. What You Will Be Doing: Define the complete software architecture for rack-scale infrastructure products and services, covering control plane services, infrastructure management, firmware, operating systems, kernel drivers, networking fabrics, accelerator software, and user-mode manageability software. Use Kubernetes and cloud-native primitives as an infrastructure fabric when appropriate. This includes controllers, operators, reconciliation loops, and open source components. These components can operate safely at rack and fleet scale. Build open source infrastructure software that can b