Senior Software Engineer - Distributed Systems Engineer, EDA Infrastructure

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. <

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. Widely considered to be one of the technology world’s most desirable employers, NVIDIA is an industry leader with groundbreaking developments in High-Performance Computing, Artificial Intelligence and Visualization. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services. Our work opens up new universes to explore, enables amazing creativity and discovery and powers what were once science fiction inventions from artificial intelligence to autonomous cars. NVIDIA’s Hardware Infrastructure organization develops, deploys, and runs engineering infrastructure on large-scale, distributed cloud computing systems with 1000s of heterogeneous servers and petabytes of high-performance shared storage. We are looking for a highly motivated senior software development engineer to develop and deploy software storage management systems for NVIDIA's worldwide design engineering teams. Collaborate with internal customers and partners, working with technical teams to build and implement efficient software storage systems that improve engineering efficiency and benefit NVIDIA's business. What you'll be doing: Collaborate with HW and SW engineering teams to define new features for distributed software storage systems. Craft and develop scalable, distributed, storage management system solutions written in Perl and Python Work closely with team members and internal customers to optimize workflow efficiency and scalability. Drive improvements to SW design and test processes to improve velocity and deliver very high production quality. Directly contribute to the overall quality of and improve time t

Tenstorrent is leading the industry on cutting-edge AI technology, revolutionizing performance expectations, ease of use, and cost efficiency. With AI redefining the computing paradigm, solutions must evolve to unify innovations in software models, compilers, platforms, networking, and semiconductors. Our diverse team of technologists have developed a high performance RISC-V CPU from scratch, and share a passion for AI and a deep desire to build the best AI platform possible. We value collaboration, curiosity, and a commitment to solving hard problems. We are growing our team and looking for contributors of all seniorities. We are looking for a senior engineer to improve the reliability, performance, and user experience of the high-performance computing infrastructure that supports silicon development. This role sits at the intersection of EDA, distributed systems, CI/CD, and developer productivity. You will help engineering teams run simulations, verification, coverage, and other silicon-development workloads with fewer failures, shorter feedback cycles, and better operational visibility. This role is hybrid, based out of Austin, TX or Santa Clara, CA. We welcome candidates at various experience levels for this role. During the interview process, candidates will be assessed for the appropriate level, and offers will align with that level, which may differ from the one in this posting. Who You Are Hold an advanced engineering degree (BS, MS, or PhD in EE, ECE, CS, or related field) paired with over seven years of hands-on industry experience. Bring expertise in Git branch strategies, merge conflict resolution, operational failure debugging, and software engineering repository management practices. Possess practical working knowledge of SQL databases, data query optimization, and modern structured data technologies. Leverage AI-assisted development tools and agentic workflows to solve complex technical problems, enhance automated testing, and streamline

NVIDIA is seeking a Senior System Architect: Heterogeneous EDA Systems to solve a complex challenge in accelerated computing: Failure Attribution at Scale. As EDA or equivalent experience workloads scale across thousands of heterogeneous nodes, a single failure can cause massive resource waste. We need an engineer to develop and build an automated framework. This framework will ingest telemetry from CPU and GPU clusters to identify the root cause of job failures in real-time. It will distinguish between hardware faults, infrastructure instability, and software defects. What you'll be doing: Architect Failure Attribution Frameworks: Build a scalable &#34;flight recorder&#34; for EDA jobs that captures high-fidelity state across the CPU, GPU, and Fabric at the moment of failure. Build automated diagnostics that correlate GPU XID errors, PCIe bus failures, and CUDA memory exceptions. Connect these errors with system-level events such as OOM kills or NUMA-related hangs. Distributed Logging & Tracing: Implement low-overhead tracing mechanisms (using tracing tools or custom agents) that provide access to job execution across multi-node Slurm or Kubernetes clusters. Root Cause Automation: Develop heuristics and models based on machine learning to classify failures as &#34;Hardware Fault,&#34; &#34;Software Bug,&#34; or &#34;Environment Issue.&#34; This reduces the Mean Time to Identify (MTTI) for R&D teams. Resiliency Engineering: Work closely with hardware and infrastructure teams to define &#34;signals of impending failure,&#34; enabling proactive job migration or check-pointing before a crash occurs. What we need to see: Distributed Systems Mastery: BS, MS, or PhD in Computer Science or Electrical Engineering (or equivalent experience) with 6&#43; years in systems programming. Experience building automated

The Continuous Profiler team at Datadog is building a cutting-edge continuous profiling product that has low enough overhead to run continuously in production, unlocking insights into how customer applications perform under real workloads at scale. As AI reshapes software development and teams lose touch with how their code works and interacts with the hardware, this capability is becoming more essential than ever, for humans and AI alike. Today the information produced by our profilers is used for everything from automatic performance tuning to root cause analysis. As a distributed systems engineer on the profiling team, you will be contributing to the systems that intake, process, index, store and make sense of the data collected. You will be working with some of the best runtime engineers, visualization experts, data scientists and designers in the industry on challenging and fun problems. Together, we contribute to runtime ecosystems such as Go and tackle novel problems, from profiling asynchronous Python applications to helping customers solve memory leaks in their applications . At Datadog, we place value in our office culture: the relationships that it builds, the creativity it brings to the table, and the collaboration of being together. We operate as a hybrid workplace to ensure our employees can create a work-life harmony that best fits them. What You’ll Do: Help design, build and contribute to the core services making the profiling product possible, at a scale that is unusual even within Datadog. Participate in multi-disciplinary squads, and iterate with customers, to bring ideas all the way from early explorations to impactful product experiences. You will be responsible for your code from development to production, and meet ambitious SLO and quality gates. You will be working with many internal and external stakeholders, working on various ways to consume the profiling data, such as the UI, agentic tools, inter

Distributed Systems engineers at Datadog design, implement and run in production the foundational platforms powering our applications. Your data pipelines will ingest, store, analyze and query in real-time billions of events per second from companies all over the globe. The platforms are optimized for durability, high availability, low latency, internet-scale footprint and operability. At Datadog, we place value in our office culture - the relationships that it builds, the creativity it brings to the table, and the collaboration of being together. We operate as a hybrid workplace to ensure our employees can create a work-life harmony that best fits them. What You’ll Do: Build fault-tolerant, horizontally scalable solutions running in multi-tenant environments Write in Go, Java Rust or C++, amongst other languages Use Kafka, Redis, Cassandra, Elasticsearch and other open-source components Own meaningful parts of our service, have an impact, grow with the company Who You Are: 6+ years of experience You have a BS/MS/PhD in a scientific field or equivalent experience You have significant backend programming experience in one or more languages (Go, Java, Rust, C++) You have been exposed to working on problems (high durability / low latency /…) You can get down to the low-level when needed You care about simple designs and performance You want to work in a fast, high-growth startup environment that respects its engineers and customers You have demonstrated ability to use AI coding tools in day-to-day workflows and validate, critique, and refine AI-generated output. Bonus: you’re motivated to push the boundaries of how AI can improve software engineering best practices and contribute to building AI-enabled products. This job is available in various departments within our company; to conform to US export control regulations, some of these roles may require candidates to be eligible for any required authorizations from the US government. Datadog values peo