Top Software Engineer Jobs

The Principal Software Engineer will lead architecture and development efforts for next-generation Data Center products, collaborating with customers and addressing complex technical issues.

The Senior Software Engineer will design and optimize communication libraries for GPU-based computing systems targeting deep learning and HPC, contributing to parallel programming interfaces and system software.

Design and develop server applications for managing NVLink networks, collaborate on new features, and oversee integration testing and QA.

Senior software engineer on Developer Tools working across frontend and backend to design, implement, and ship profiling features for cloud and cluster environments. Collaborate with cross-functional teams, interact with users, and deliver end-to-end product features from prototypes to demos focused on collecting, analyzing, and visualizing performance data.

The role involves architecting and operating NVIDIA's cloud-native attestation platform, developing high-availability services, collaborating with cross-functional teams, and mentoring engineers.

Develop optimization engines for VLSI physical design at NVIDIA, focusing on algorithms in C++ for timing optimization, with significant hardware and software collaboration.

Develop production-grade integrations for NVIDIA Grove across AI frameworks including Dynamo, llm-d, Ray, and PyTorch. Build adapters, plugins, operators, runtime components, reference workflows, and developer tooling. Optimize distributed multi-node and multi-GPU training and inference, improve Kubernetes observability, define APIs, and troubleshoot containers, networking, scheduling, CUDA, and framework behavior. Collaborate with engineering teams and open-source communities to upstream maintainable changes and ensure compatibility across rapidly evolving dependencies.

The Senior Software Performance Engineer will optimize NVIDIA's autonomous driving solutions, enhance software architecture efficiency, and develop performance-driven product code in C++, collaborating with multiple teams.

Principal-level software engineering leader responsible for architecting, building, and operating reliable enterprise AI applications and agent infrastructure. The role spans frontend, backend, distributed services, data flows, deployment, observability, testing, release engineering, security, and incident response. Responsibilities include developing reusable orchestration, memory, evaluation, telemetry, and policy capabilities; operationalizing NVIDIA AI technologies; defining secure agent collaboration architecture; mentoring engineers; and partnering cross-functionally to deliver production-grade AI systems.

The role involves optimizing CUDA kernels for geometric neural networks, building interfaces for PyTorch and JAX, and collaborating with research teams to develop GPU-accelerated software for scientific applications.

Lead the optimization and performance analysis of distributed training and inference workloads on NVIDIA GPU platforms, with responsibilities including debugging, benchmarking, and ensuring reliability of large-scale AI systems.

As a Principal Software Engineer, you'll develop software systems for NVIDIA's rack-scale infrastructure, defining software architecture, collaborating across teams, mentoring engineers, and ensuring high-quality technical decisions in complex environments.

Build and maintain scalable 3D simulation software for digital twins and synthetic data generation. Develop backend services, AI agents, distributed computing solutions, cloud applications, and frontend components. Optimize performance and reliability, collaborate with product and cross-functional teams, participate in code reviews, and guide innovative solutions involving physical AI, computer graphics, and deep learning.

Build and optimize CUDA kernels for molecular dynamics simulations, deliver GPU-accelerated math primitives, integrate them into major simulation engines, and develop CI/CD systems for performance testing across NVIDIA GPUs. Collaborate with applied scientists to productionize algorithms and support external partners. The role requires deep C/C++ and CUDA expertise, high-performance computing experience, familiarity with molecular dynamics, and experience developing scientific software or GPU APIs.

Develop and scale autonomous vehicle simulation platforms supporting training, testing, validation, and debugging across millions of scenarios. Build Real2Sim and Sim2Real domain adaptation workflows, optimize distributed simulation infrastructure, and contribute across computer vision, deep learning, neural rendering, generative AI, and synthetic data systems. Collaborate with researchers, infrastructure engineers, and product teams to transfer technologies into production and open-source projects.

Benchmark and optimize deep learning model inference and training performance on NVIDIA GPUs. Develop tooling for automated benchmarking, analysis, and performance optimization of CUTLASS kernels. Identify gaps between theoretical and realized performance, recommend software or model improvements, and serve as the team’s kernel-performance authority across GPU architecture, deep learning frameworks, and QA teams.

Design and build end-to-end Real2Sim pipelines and neural 3D reconstruction systems that convert multi-sensor data into simulation-ready scene and object assets. Integrate outputs with mapping, localization, and multi-sensor fusion, collaborate with ML researchers, deploy and optimize large-scale data-generation and augmentation pipelines, and validate and monitor production reconstruction and simulation services.

Lead validation of ML datacenter stacks from cluster to rack scale for CSPs: define test strategies, reproduce and triage customer bugs, validate fixes, run perf benchmarks, manage test data and tooling, produce release-readiness reports, and collaborate with internal and partner engineering teams.

Design, develop, and optimize a low-latency, high-quality media streaming stack for cloud gaming. Analyze GPU/CPU video pipelines, isolate bottlenecks, collaborate with hardware teams, build tools to measure video quality, and implement adaptive streaming and codec-based improvements for resilient performance under variable networks.

Lead and implement end-to-end, AI-enhanced employee experiences across web and mobile. Architect scalable services and UIs, embed LLM/vector-search and recommendation systems, integrate third-party HRIS and collaboration platforms, champion modern front-end patterns and cloud-native back-end practices, ensure observability and reliability, and mentor engineers while upholding security, privacy, and accessibility standards.

Build and maintain network automation and orchestration software for NVIDIA GPU Cloud and SuperPod deployments. Design/configure centralized configuration management, integrate cloud provider APIs, implement data pipelines and streaming telemetry, and automate lifecycle operations from procurement to deployment. Collaborate with infrastructure experts to deliver zero-touch, production-grade network provisioning, monitoring, and management tools.

Leads two scrum teams while remaining hands-on in software development at least 50% of the time. Designs, codes, tests, debugs, and supports secure, resilient, high-throughput distributed payment systems using Java and Spring Boot. Drives AI-assisted engineering practices, automation, CI/CD, code quality, operational stability, and responsible AI usage. Evaluates technical solutions with vendors and internal teams and leads engineering communities of practice.

Leads hands-on software development and technical direction for scalable analytics and data platforms, including SAS Viya and Databricks across on-premises and AWS environments. Designs distributed applications, drives CI/CD, automation, security, reliability, and modernization, and partners with product and engineering teams throughout the SDLC. Coaches engineers, manages incidents and operational readiness, establishes engineering standards, and uses delivery and production metrics to improve quality and performance.

Develop and debug high-performance C applications on multicore Linux systems for ultra-low-latency electronic trading infrastructure. Lead software design and delivery, address complex engineering challenges, define testing strategies, mentor engineers, improve development practices, and collaborate directly with customers. The role requires expertise in concurrency, multithreading, socket programming, TCP, and Linux application development, with full-time onsite work in Needham, Massachusetts.

Design, build, and own large-scale segmentation systems, data pipelines, APIs, and customer-facing dashboard experiences. Lead architecture and product decisions, improve performance, reliability, and cost, mentor engineers, collaborate cross-functionally, and maintain operational procedures for systems processing billions of users daily. The role requires hands-on technical leadership across distributed systems, high-throughput data platforms, and customer-facing products.