Top Software Engineer Jobs

Develop and scale production AI infrastructure supporting large GPU clusters and workloads. Responsibilities include Kubernetes operator and API development, GPU resource scheduling, cluster operations, node health monitoring, telemetry-based reliability improvements, incident analysis, and distributed systems engineering. The role collaborates across teams to improve the performance, availability, and scalability of NVIDIA’s DGX Cloud platform.

Develop platform and system software for NVIDIA GPU and SOC products, including kernel device drivers, firmware interactions, health monitoring, observability platforms, backend data pipelines, and production applications. Build scalable metrics, logging, and tracing systems while collaborating across software, cloud, data center, and product teams. Apply expertise in Linux internals, operating systems, computer architecture, concurrency, performance profiling, CPU-GPU interactions, PCIe, memory hierarchy, and AI workloads.

Develop GPU display system software from proof of concept through production, including architecture, algorithms, implementation, optimization, and debugging. Design features based on DisplayPort, HDMI, and eDP standards; influence GPU, SoC, and platform architecture; collaborate with hardware, software, and OEM/ODM teams; and deliver solutions under tight deadlines.

Develop GPU code generation and compiler optimizations for TensorRT to improve generative AI inference performance. Responsibilities include operator fusion, scheduling, specialization, profiling, debugging, numerical validation, and performance analysis. The role requires C++ software development and GPU architecture expertise, with collaboration across hardware, compiler, kernel, and deep learning teams. Preferred experience includes CUDA, MLIR, GPU kernels, Tensor Core optimization, inference frameworks, and low-level NVIDIA performance tools.

Develop and maintain upstream Linux kernel support for NVIDIA GPU virtualization. Build virtualization features in Rust and C, contribute patch series to the Linux kernel, and work across KVM, QEMU, VFIO, PCIe SR-IOV, memory management, and device lifecycle systems. Debug complex host, hypervisor, driver, firmware, performance, and reliability issues while collaborating with maintainers, partners, and distributed engineering teams.

Lead the technical direction, architecture, development, testing, and global deployment of NVIDIA’s AI agent compute and virtual desktop platforms. Build secure, isolated, cloud-hosted compute services across AWS and Azure, and drive desktop streaming performance for GPU and general-purpose workloads. Partner with engineering, IT, and security leaders to develop a multi-year platform roadmap supporting AI agents and end users.

Build and maintain production software for bare-metal GPU infrastructure across provisioning, hardware validation, firmware management, auditing, remediation, and production handoff. Integrate Redfish and vendor BMC interfaces, automate failure detection and recovery, debug issues spanning hardware, firmware, Linux, networking, and provisioning, and collaborate with vendors and infrastructure teams. The role also uses AI agents and LLM tools extensively for software development, testing, code review, and failure investigation.

Develop and integrate networking software for NVIDIA BlueField and ConnectX platforms supporting large-scale AI infrastructure. Work with customers and internal teams on RDMA and RoCE solutions, troubleshooting issues across applications, libraries, drivers, firmware, and switches. Analyze performance metrics, create automation and test cases, validate fixes, and support deployments from testing through production while documenting technical findings and decisions.

Lead the architecture and development of scalable APIs, control planes, workflow orchestration, and fleet lifecycle automation for NVIDIA DGX Cloud. Drive integration across cloud systems, Kubernetes, Slurm, and observability platforms while improving reliability and reducing manual operations. Provide technical leadership, mentor senior engineers, influence organizational strategy, and deliver high-scale distributed infrastructure for AI and cloud computing.

Design, develop, optimize, and debug C++ software for ultra-low-latency WebRTC streaming. Implement real-time transport, security, media, and congestion-control protocols; contribute to standards and protocol solutions; define streaming quality KPIs; and integrate software with platform, connectivity, automotive, and hardware systems.

Build and scale evaluation and analysis infrastructure for autonomous vehicle development. Design distributed tooling, services, APIs, CI pipelines, data contracts, schemas, and UI-facing workflows that help AV and ML engineers analyze logs, assess model behavior, debug regressions, and produce metrics. Improve observability, performance, reliability, rollout guardrails, and production readiness while collaborating across AV engineering, data platform, infrastructure, and product teams.

Design and maintain software that models data center power, total cost of ownership, and performance trade-offs across hardware, software, workloads, and deployments. Contribute to architecture, prototyping, testing, regression coverage, debugging, code reviews, and cross-functional interface design. Help resolve complex technical problems, improve engineering practices, and support teammates in adopting effective technologies and processes.

Lead development and optimization of NVIDIA’s Aerial platform for software-defined 5G/6G RAN systems. Develop baseband software in C, C++, Python, and CUDA; optimize CPU, GPU, and NIC performance; troubleshoot and integrate complex modules; conduct testing, demonstrations, benchmarking, and lab trials; and collaborate with engineering teams, customers, and telecommunications partners on system integration and technical solutions.

Build and scale NVIDIA’s AI infrastructure platform for GPU provisioning, configuration, lifecycle management, monitoring, health management, and distributed-system qualification. Develop automation for data center operations and machine learning systems, integrate hardware through AI applications, and work with GPU diagnostics, cluster and network telemetry, Kubernetes or SLURM, Linux, and fault-tolerant distributed architectures.

Develop and maintain automated integration and deployment for components in a Kubernetes-based software stack. Manage third-party dependencies, validate compatibility, maintain integration workflows, troubleshoot cross-component issues, and ensure compatibility with NVIDIA hardware. The role requires extensive Linux software development experience, Python proficiency, Kubernetes and container expertise, and collaboration with QA, CI/CD, development teams, and upstream projects.

Define the technical strategy and architecture for NVIDIA’s autonomous, AI-driven enterprise IT operations. Build production agentic systems using LLMs, tool calling, RAG, orchestration, and human-in-the-loop safeguards. Lead integration platforms across ServiceNow, ERP, procurement, and endpoint systems while establishing standards for reliability, evaluation, observability, security, and governance. Drive cross-organizational technical alignment, make major architecture decisions, mentor senior engineers, and scale adoption of agentic platforms.

Owns the design, development, and operation of Kubernetes management-plane software for customer-managed AI and HPC infrastructure. Responsibilities include building cluster lifecycle capabilities, Kubernetes controllers and operators in Go, automated provisioning, distributed-system reliability and security improvements, CI/CD and testing, technical leadership, cross-team coordination, mentoring, and production troubleshooting.

Design and build scalable software for NVIDIA’s multi-tenant cloud infrastructure, including network and systems automation, hardware and network provisioning, APIs, testing, deployment, and production services. Collaborate with engineers, architects, product managers, internal partners, and leadership to deliver robust distributed systems and contribute to open-source communities.

Lead architecture and roadmap for Azure PostgreSQL systems; design and review scalable, maintainable database engine features (query processing, storage, transaction logging, recovery, versioning, IO, scale-out). Influence coding standards and run code reviews, write proof-of-concept or production code, contribute to open-source PostgreSQL/Linux, and participate in rotational on-call operations.

Design and develop a scalable agent runtime platform for Microsoft 365 Copilot and AI agents. Responsibilities include system architecture, cross-team technical design, AI-native engineering, code reviews, debugging, observability, safe deployments, incident response, and improving reliability, security, performance, and maintainability. The role supports long-running tasks, extensibility, multi-agent collaboration, and LLM-powered workflows.

Principal Software Engineer responsible for designing, coding, testing, deploying, and operating AI-powered applications, agents, integrations, and workflows. Leads architecture, reliability, observability, security, compliance, experimentation, and production support while mentoring engineers and coordinating cross-team delivery. The role requires building scalable, maintainable solutions, managing dependencies, driving engineering excellence, and responding to live-site incidents through on-call responsibilities.

Design, develop, and maintain secure, scalable full-stack applications using Java, Spring Boot, Angular, Kafka, REST APIs, microservices, and cloud-native practices. Build event-driven systems, optimize databases, troubleshoot production issues, improve reliability and performance, participate in Agile engineering practices, and mentor engineers. Translate business requirements into maintainable technical solutions while applying security, testing, CI/CD, observability, and operational excellence standards.

Maintain, debug, enhance, and modernize over 200 Visual Basic manufacturing applications within a Manufacturing Execution System. Integrate automation systems with PLCs, Hirata, and iFIX, support production operations, lead legacy-code migrations, implement change management and documentation standards, and collaborate with IT and manufacturing business teams.

Lead architecture and implementation of LangSmith platform features across Go, Python, and TypeScript. Collaborate with product and design teams, mentor engineers, drive project timelines, and deliver clean, tested software. Build and scale APIs and data-intensive applications using backend and frontend technologies, PostgreSQL, and Redis while engaging with enterprise customers and internal stakeholders.

Design, develop, test, and deploy software or firmware components within an Agile team. Create prototypes, automate deployment and tests, resolve production issues, maintain documentation, participate in code reviews, and follow security and modern delivery practices.