Senior Infrastructure Software Engineer Jobs

NVIDIA is looking for an experienced software engineer with infrastructure experience to become a senior member of the Cloud Foundations Automation - Development Team. We build and manage the automation ecosystem supporting NVIDIA's GPU Cloud and NVIDIA SuperPod deployments. NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most hard-working and dedicated people on the planet working for us. If you're creative and autonomous, we want to hear from you! What you'll be doing: Developing software to enable efficient network design, deployment and day 2 management. Building product focused software solutions, used by internal and external customers. Helping us as we transform our workflows and organization into a centrally orchestrated configuration management framework, operating at scale across geographies. Owning and driving integrations with various service APIs such as Cloud Service Providers, to automate creation of environments and auto populate data sources in turn. Building on open source software, designing and implementing data structures and UI interfaces to automate processes from equipment purchase to device config generation to deployment to operations. Streamlining deployment mechanisms and life cycle operations Developing modern service architectures around streaming data and event pipelines. Working with infrastructure domain experts on true, zero touch deployment solutions and utilizing best of breed high performance computing management solutions. Be a proactive problem solver, looking out for new opportunities to improve our services and customer experience. Communicate readily with your peers across the organization, b

About the Role Chime’s Data Platform team builds the infrastructure every engineering and analytics team depends on - ingestion, transformation, quality, governance, and self-serve tooling across batch and streaming workloads. You’ll own core platform systems at scale, design the frameworks other teams build on, and set the technical standards for how data moves through Chime. High autonomy, real architectural trade-offs, production impact from day one. We’re hiring multiple engineers into this role at the senior level. In this role, you can expect to Design, build, and operate self-serve ETL/ELT frameworks supporting both batch and streaming workloads, with direct ownership of pipeline reliability, data quality SLAs, and schema evolution Drive the technical roadmap for our data platform - evaluate build-vs-buy decisions, define integration patterns, and set standards that engineering teams across the company adopt Partner with Product Engineering, Data Science, Analytics, and Marketing as a technical lead to design data contracts, onboard new data domains, and scale ingestion without sacrificing governance Architect and enforce data lineage, schema registry, and access controls across all domains - ensuring compliance with fintech regulatory requirements (SOX, PII handling) Own the platform - monitor, alert, debug, and resolve incidents across the data platform with the urgency expected of infrastructure that powers financial products Raise the technical bar of the team through code review, design reviews, and hands-on mentorship of junior and mid-level engineers Engineers are required to participate in on-call rotation. Being on call may include responding to incidents outside of regular working hours when necessary. To thrive in this role, you have 5+ years building, shipping, and operating data infrastructure in production - not just writing pipelines, but owning their reliability, performance, and cost at scale Strong system design skills - you’ve written desig

We are seeking a Senior Software Engineer with strong infrastructure expertise to design, build, and operate the next generation of our enterprise Observability, Automation, and AI-driven Reliability Platform. This role will build highly scalable distributed systems and platform services spanning Storage, Compute, Network, VMware, OpenShift, and bare-metal infrastructure. The engineer will help transform infrastructure operations from reactive monitoring and manual remediation to proactive, predictive, and AI-driven autonomous operations. What You Will Be Doing: Design, build, and operate distributed software platforms for enterprise observability, telemetry, automation, and infrastructure reliability at large scale. Develop reusable platform services, APIs, automation frameworks, and control planes that enable self-service, reduce operational toil, and automate infrastructure operations across multiple engineering teams. Build scalable telemetry and event-processing systems spanning metrics, logs, traces, events, topology, and alerts, with the performance and efficiency to process billions of infrastructure signals. Build intelligent and AI-native reliability capabilities, including agentic workflows for anomaly detection, forecasting, root-cause analysis, automated debugging, and closed-loop remediation. Drive technical architecture and engineering direction across Storage, Compute, Network, and Platform domains, solving complex and ambiguous problems that span multiple teams. Engineer for production at scale, with strong focus on software quality, scalability, security, performance, observability, maintainability, and operational readiness. Provide technical leadership and mentorship, influence engineerin

Why Harvey At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. Role Overview Harvey is building the AI platform trusted by the world’s leading law firms and enterprises. Our infrastructure is the foundation that powers every customer interaction, every model inference, and every production workload. We’re looking for a Production Engineer to help build and operate Harvey’s core compute and networking infrastructure, Kubernetes platform, workflow orchestration platform, and production infrastructure foundations. You’ll work on the systems that enable engineering teams to move quickly and operate reliable services at scale. In this role, you’ll improve the reliability, scalability, security, and efficiency of Harvey’s infrastructure platform. You’ll solve complex production challenges across compute fleet management, capacity planning, infrastruc

Why Harvey At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. Role Overview Harvey is building the AI platform trusted by the world’s leading law firms and enterprises. Our infrastructure is the foundation that powers every customer interaction, every model inference, and every production workload. We’re looking for a Production Engineer to help build and operate Harvey’s core compute and networking infrastructure, Kubernetes platform, workflow orchestration platform, and production infrastructure foundations. You’ll work on the systems that enable engineering teams to move quickly and operate reliable services at scale. In this role, you’ll improve the reliability, scalability, security, and efficiency of Harvey’s infrastructure platform. You’ll solve complex production challenges across compute fleet management, capacity planning, infrastruc

What we're building Mutiny is the self-improving AI infrastructure for GTM teams to execute faster and close more revenue. Our ambition is to do for revenue velocity what Cursor and Claude Code did for engineering velocity. With Mutiny, everyone in sales and marketing gets a bench of GTM athletes that handle any work across their revenue motion and learn from what's actually moved their deals. In April we re-launched the product as an agent-first platform. Anthropic showcased us as a leader in AI GTM. MRR is growing more than 70% month-over-month, with customers like Uber, Rippling, and Snowflake. We're backed by Sequoia, YC, and Insight, and we're building a generational company. The opportunity Most engineers spend their career making predictable systems faster. You'll spend yours making non-deterministic ones trustworthy. As a senior engineer on our AI product team, you'll architect the Campaign Builder and Agent experiences marketers and sellers open every day to go from idea to personalized assets in minutes. You'll partner directly with product, design, and the founders to define what an agent-first GTM platform should feel like, and your calls on architecture, evals, and guardrails compound across thousands of customer accounts. This role is in person in New York City, five days a week, and we ship weekly. What you'll own The core agent surfaces. Architect and ship the Campaign Builder and Agent experiences end-to-end. Frontend, backend, prompts, evals, the whole stack. Reliability on top of LLMs. Make non-deterministic models feel deterministic at the surface. Build the retries, fallbacks, and orchestration so the customer never sees the failure mode. Evals and guardrails. Define how we measure quality, catch regressions, and keep brand and tone consistent across thousands of customer accounts. Speed and feel. AI products live or die by latency and the loop between intent and output. You'll obsess over both, and use coding agents and agent networks to ship f

NVIDIA is seeking a senior systems software engineer to help engineering teams run demanding build, simulation, and validation workloads efficiently and reliably. Our Workload Systems Engineering team connects customer workflows with the infrastructure that performs them. We develop self-service mechanisms and clear diagnostics, investigate performance and reliability across system boundaries, and deliver measured infrastructure improvements with the teams that own the underlying components. Your responsibilities include enabling application configuration, remote execution, workers, hosts, GPU and CPU resources, storage, data paths, and on-target systems. The position requires direct system evaluation: diagnosing causes of slow or failing workloads, trying alternatives in representative scenarios, applying improvements with others, and verifying customer advantages. What you'll be doing: Build shared self-service and validation mechanisms that let engineering teams configure and use supported infrastructure workflows with less manual assistance. Give customers and on-call teams useful diagnostic views across application, submission, remote-execution, and host or target layers so they can identify where failures and slowdowns originate. Set performance and reliability benchmarks for representative workloads. Apply profiling, metrics, traces, and controlled experiments to pinpoint the changes that make a difference. Evaluate and improve compute and GPU selection, scheduling and placement, concurrency, worker and runner configuration, resource sharing, caching, and input/output paths. Make retry causes, recovery outcomes, and wasted execution capacity visible. Work with component owners to eliminate recurring failures and unnecessary re-execution. Implement and validate infrastructure-level changes with application, build and CI, remote-execution, and infrastructure teams. Measure

We are looking for a Sr. Engineer to design, build, and scale the infrastructure powering NVIDIA’s AI agent ecosystem. You will work at the intersection of distributed systems, developer platforms, and agentic AI — building the foundational services that enable teams across the company to develop, deploy, orchestrate, and operate autonomous AI agents at production scale. What you will be doing: Build and develop platform services that manage the full agent lifecycle from registration through deployment, execution, and teardown Architect multi-tenant Kubernetes execution environments with pod lifecycle management, per-tenant namespace isolation, persistent storage, admission policy enforcement, and identity propagation Develop and maintain automated CI/CD pipelines using GitLab CI and ArgoCD, including reusable pipeline templates and deployment blueprints that standardize how agents are built across teams Build framework-agnostic infrastructure supporting multiple agent SDKs and harnesses — lifecycle hooks, skills configuration, memory services, and OTEL instrumentation — so teams can onboard new agent frameworks without platform changes Build and operate Kafka-based message pipelines and real-time event streaming using Redis PubSub and SSE Develop data ingestion pipelines, access interfaces, and storage layers that power AI agent knowledge and context Implement session management for state persistence, conversation history, and agent recovery across sessions Develop multi-layer auth using OAuth 2.0, JWT validation, token exchange, and gateway integration, and manage secrets lifecycle with Vault (provisioning, rotation, container injection) Partner with security teams on compliance, access controls, and approval workflows for agent operations What we need to see: Bachelor's or Master's degree in Computer Science, Engineering, or

The NVIDIA DGXC Data Services team builds cloud-native systems, frameworks, and services for managing data across hybrid and multi-cloud infrastructure. We are building the next-generation data and storage infrastructure to solve some of the hardest problems in AI: storage, access, ingestion, governance, observability, and data management for exabyte-scale, high-performance GPU-based training and inference jobs. Our work gives NVIDIA teams the foundational capabilities they need to build, train, deploy, and operate AI products at scale without reinventing critical data infrastructure for every workload. What you will be doing: Build storage technologies, client libraries, and filesystem frameworks that help AI workloads access data across object stores, file systems, and hybrid cloud infrastructure. Develop high-performance storage paths for training and inference workflows, including data loading, checkpointing, caching, POSIX-style access, and object-store integration. Build observability systems that diagnose storage bottlenecks, attribute GPU idle time to I/O behavior, and expose actionable telemetry through production monitoring stacks. Improve performance, scalability, and reliability of storage systems serving massive datasets, deep directory trees, and high-concurrency AI workloads. Work closely with internal AI teams, platform teams, SRE, and operations to validate storage behavior against real workloads and production environments. Use modern software engineering practices, including AI-assisted and agentic development workflows, while maintaining high standards for design, testing, security, performance, and verification. What we need to see: BS in Computer Science, Information Sys

The NVIDIA DGXC Data Services team builds cloud-native systems, frameworks, and services for managing data across hybrid and multi-cloud infrastructure. We are building the next-generation data and storage infrastructure to solve some of the hardest problems in AI: storage, access, ingestion, governance, observability, and data management for exabyte-scale, high-performance GPU-based training and inference jobs. Our work gives NVIDIA teams the foundational capabilities they need to build, train, deploy, and operate AI products at scale without reinventing critical data infrastructure for every workload. What you will be doing: Build cloud-native data and storage services for hybrid and multi-cloud infrastructure, including dataset discovery, ingestion, governance, checkpointing, observability, and low-latency access. Develop scalable cloud-native services and APIs that support exabyte-scale, high-performance GPU training and inference workflows. Work closely with product managers, internal AI teams, platform teams, and partner engineering teams to understand requirements and turn them into reliable production systems. Collaborate with SRE, operations, and support teams to improve service reliability, performance, observability, on-call readiness, and operational scale. Use modern software engineering practices, including AI-assisted and agentic development workflows, while maintaining high standards for design, testing, security, and verification. What we need to see: BS in Computer Science, Information Systems, Computer Engineering, or equivalent experience, with 5+ years of software engineering experience. Strong foundation in algorithms, data structures, distributed systems, and practi

We are looking for a Senior Staff Software Engineer to play a key leadership role in our Developer Productivity group and drive developer velocity and fundamentally change the software development lifecycle with AI. The scope spans across the stack including tooling, frameworks, and infrastructure to provide full leverage in the developer experience for every software developer at Asana. Some of the teams’ current work include building out our internal agentic harness, setting up our online/offline developer evals, and increasing the effectiveness of our automated validation loop. The importance and scope of this work has grown significantly in the last year, and we need someone operating at the group level to help sequence and execute it. This role can be based in our New York or San Francisco offices with an office-centric hybrid schedule. The standard in-office days are Monday, Tuesday, and Thursday. Most Asanas have the option to work from home on Wednesdays. Working from home on Fridays depends on the type of work you do and the teams with which you partner. If you're interviewing for this role, your recruiter will share more about the in-office requirements. What you’ll achieve Drive critical developer productivity and AI developer tooling projects that impact 500+ developers at Asana Partner with the Developer Productivity Group Tech Lead to define technical vision and strategy for 6+ engineering teams, all focused towards improving velocity for Asana engineers Build and align on the roadmap for Asana's development platform to drive faster development lifecycle feedback loops and uplevel how every Asana engineer ships Own key outcomes like extending the internal tooling engineers use to delegate work to agents across harnesses Mentor senior and staff-level engineers in Developer Productivity and across Foundations and the rest of the Engineering organization Act as a resource for team-level tech leads on topics like longer-term technical planning, design &amp

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. Senior Software Engineer — Cortex Training The Snowflake ML Platform team's mission is to let customers run their most demanding ML/AI workloads inside Snowflake. Cortex Training is our LLM post-training platform: it turns scarce, expensive GPU capacity into a simple, composable service, so customers can adapt open-weight foundation models to their own business problems while we handle the hard distributed-systems parts, including scheduling, orchestration, multi-node training and inference, fault tolerance, and throughput. The platform already runs post-training at scale. Under the hood, it decouples GPU computation from the training loop and exposes it as primitive APIs that compose into everything from SFT to full RL workflows. You'll work alongside a team that ships fast & sweats reliability and the researchers behind DeepSpeed. We're looking for an engineer who thrives in the ML infrastructure layer and brings a solid understanding of LLMs and post-training to help us scale and grow it. YOU WILL: Design and build across the full stack — from the public training APIs and SDK through the control plane to the GPU data plane. Scale the distributed systems that make GPU compute serverless — multi-tenant scheduling, placement, and capacity-aware routing across regional G

We are looking for a Senior Staff Software Engineer to play a key leadership role in our Developer Productivity group and drive developer velocity and fundamentally change the software development lifecycle with AI. The scope spans across the stack including tooling, frameworks, and infrastructure to provide full leverage in the developer experience for every software developer at Asana. Some of the teams’ current work include building out our internal agentic harness, setting up our online/offline developer evals, and increasing the effectiveness of our automated validation loop. The importance and scope of this work has grown significantly in the last year, and we need someone operating at the group level to help sequence and execute it. This role can be based in our New York or San Francisco offices with an office-centric hybrid schedule. The standard in-office days are Monday, Tuesday, and Thursday. Most Asanas have the option to work from home on Wednesdays. Working from home on Fridays depends on the type of work you do and the teams with which you partner. If you're interviewing for this role, your recruiter will share more about the in-office requirements. What you’ll achieve Drive critical developer productivity and AI developer tooling projects that impact 500+ developers at Asana Partner with the Developer Productivity Group Tech Lead to define technical vision and strategy for 6+ engineering teams, all focused towards improving velocity for Asana engineers Build and align on the roadmap for Asana's development platform to drive faster development lifecycle feedback loops and uplevel how every Asana engineer ships Own key outcomes like extending the internal tooling engineers use to delegate work to agents across harnesses Mentor senior and staff-level engineers in Developer Productivity and across Foundations and the rest of the Engineering organization Act as a resource for team-level tech leads on topics like longer-term technical planning, design &amp

Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators. At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device. We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there. A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone. As a Senior Software Engineer on the Fleet Management team, you will lead the systems that provision and rebuild Roblox's global fleet across bare metal and cloud. This team owns provisioning, and MAPI, the global Machine API that turns raw capacity into production-ready infrastructure in minutes, across hundreds of thousands of machines on-prem and cloud environments, including new GPU and new AI infrastructure. You will shape the technical direction for this critical compute platform and unify diverse hardware and environment-specific workflows behind MAPI and drive large-scale maintenance operations like firmware updates and hardware tuning. You will: Lead the Machine Bootstrap pod in building and evolving provisioning and fleet management at massive scale. Ship fleet-wide maintenance operations (BIOS updates, firmware updates, configuration changes) to hundreds of thousands of machines through MAPI. Drive best-in-class provisioning performance, minutes to fully rebuild a machine from scratch. Evaluate and integrate new hardware platforms including GPU servers and AI accelerators into the provisioning pipeline. Collaborate across Compute, Networking, and Cloud teams on the full machine l

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. Senior Software Engineer — Cortex Training The Snowflake ML Platform team's mission is to let customers run their most demanding ML/AI workloads inside Snowflake. Cortex Training is our LLM post-training platform: it turns scarce, expensive GPU capacity into a simple, composable service, so customers can adapt open-weight foundation models to their own business problems while we handle the hard distributed-systems parts, including scheduling, orchestration, multi-node training and inference, fault tolerance, and throughput. The platform already runs post-training at scale. Under the hood, it decouples GPU computation from the training loop and exposes it as primitive APIs that compose into everything from SFT to full RL workflows. You'll work alongside a team that ships fast & sweats reliability and the researchers behind DeepSpeed. We're looking for an engineer who thrives in the ML infrastructure layer and brings a solid understanding of LLMs and post-training to help us scale and grow it. YOU WILL: Design and build across the full stack — from the public training APIs and SDK through the control plane to the GPU data plane. Scale the distributed systems that make GPU compute serverless — multi-tenant scheduling, placement, and capacity-aware routing across regional G