Top Software Engineer Jobs

Provides expert technical support for Zebra Android mobile computing products, debugging AOSP Framework, BSP, Telephony, RIL, and cellular modem issues. Analyzes logs and network traffic, resolves interoperability and performance problems with carriers and chipset vendors, and collaborates with customers and internal teams. Develops AI-driven diagnostic and workflow automation tools, documents solutions, mentors support engineers, and contributes to product improvements.

Design and implement software, automation, or AI-driven solutions that improve engineering workflows, reduce manual effort, and enhance customer outcomes. Responsibilities include analyzing data across systems, collaborating with engineers, developing and testing solutions, documenting implementations, measuring project impact, and presenting results to leadership. The internship is based in Mayfield Heights, Ohio, with potential part-time remote work during the school year.

Develops, validates, documents, and maintains R programs, packages, Bash utilities, Linux tools, and statistical analysis datasets for clinical research. Translates SAS programs into R, supports metadata repositories and CDISC standards, troubleshoots complex programming issues, trains researchers, mentors programmers, and collaborates with statisticians, epidemiologists, clinicians, and data managers. The role also contributes to programming standards, artificial intelligence applications, presentations, recruitment, and research network initiatives.

Build and operate production backend services supporting Supabase AI products, including the MCP server. Responsibilities include service architecture, deployments, scaling, authentication and authorization, observability, reliability targets, on-call incident response, rate limiting, capacity management, and API evolution. The role requires strong TypeScript or Go skills, production backend experience, distributed-systems debugging, and collaboration with Platform and SRE teams in a fully remote environment.

Build and scale optimization and backend systems for public transportation planning and scheduling products. Own features from concept through production, improve performance and reliability, collaborate with designers, data scientists, researchers, product managers, transit planners, and customers, and contribute to engineering architecture, best practices, tooling, and code quality. The role involves computationally hard systems, geospatial computation, machine learning, and AI.

Supports and enhances ATIS/ATMS systems for a 24/7 transportation management center. Develops Python and Java applications, web tools, APIs, dashboards, reports, database solutions, automation, and integrations. Maintains applications across Windows and Linux environments, troubleshoots operational issues, manages deployments and documentation, and supports system improvements involving analytics, AI, GIS, and ITS technologies. Provides technical guidance, training, and coordination with transportation personnel, vendors, and engineering teams.

Principal Software Engineer leading architecture and technical strategy for an in-vehicle AI assistant platform. The role spans Android Automotive client applications, cloud services, edge-cloud orchestration, conversational AI, gRPC, streaming interfaces, testing, telemetry, and distributed systems. Responsibilities include cross-functional architectural alignment, roadmap development, resolving complex reliability and privacy trade-offs, integrating AI frameworks, and mentoring senior engineering teams.

Develop HPC and AI software workflows for experimental data processing, imaging, plasma, high-energy physics, and materials simulations. Profile applications, benchmark performance, identify bottlenecks, and advise collaborators and product teams. Build tutorial examples, translate research into software tools, and represent NVIDIA at scientific conferences and industry events.

Leads the design, development, and deployment of scalable, reliable software and infrastructure solutions. Builds AI agents, automation workflows, observability integrations, and monitoring systems using technologies including C++, Python, Java, Claude, Codex, BigPanda, ITMP, and Prometheus. Collaborates across teams, drives engineering standards, mentors junior engineers, and manages complex end-to-end projects.

Design and develop next-generation scale-up networking fabrics for AI data centers. Lead collaborative development across hardware, software, and applications to improve scalability, reliability, and robustness for large-model training. Create innovative concepts through production implementation, collaborate with cross-functional teams, and lead complex projects delivering large-scale networking and AI solutions.

Architects and delivers enterprise integration and API platforms using event-driven, asynchronous, cloud, and AI-augmented workflows. Develops reusable frameworks across SAP, SaaS, and partner ecosystems; governs APIs; integrates EDI, REST, GraphQL, and SOAP systems; and builds autonomous, self-monitoring and self-healing workflows. The role also requires programming, identity and access control expertise, CI/CD automation, observability, and close collaboration with Product and Engineering teams.

Design and implement GPU performance profiling tools focused on AI applications. Build features used by CUDA developers, collaborate with NVIDIA compiler and CUDA driver teams, and influence tool design. The role requires strong C++ software engineering experience, GPU hardware knowledge, and familiarity with performance tools, with preferred expertise in CUDA kernel programming, profiling, and DWARF parsing.

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.

Design and implement AI-driven optimizations for software development workflows, leveraging LLMs and Agentic AI to enhance productivity and reliability.

Develop and optimize GPU-accelerated numerical linear algebra libraries for AI and high-performance computing. Design kernel generation, JIT compilation, matrix operations, and operator fusions; lead projects involving multiple engineers and teams; provide technical mentorship; collaborate with product management and customers on requirements and roadmaps; and improve performance and maintainability through architectural enhancements.

Lead a team to optimize algorithms for Deep Learning and Machine Learning, define technical initiatives, and collaborate with various teams for hardware and software innovation.

Design and develop a C++-based system for simplifying and accelerating computing in DL and HPC, alongside evaluating performance and improving library quality.

Owns end-to-end architecture and implementation of high-performance communication and memory-management libraries for distributed AI. The role drives GPU, DPU, NIC, and switch hardware-software co-optimization; profiles data movement across memory, storage, and networks; integrates networking into AI serving frameworks; contributes to open source; mentors engineers; leads design reviews; and prototypes emerging technologies.

Designs and scales observability and telemetry platforms for distributed AI and HPC environments. Builds high-performance backend services, OpenTelemetry components, metrics pipelines, and real-time or batch data systems. Improves reliability, performance, capacity, and cost efficiency while developing monitoring, alerting, and service reliability frameworks. Requires production distributed-systems expertise, programming skills, time-series data experience, Kubernetes knowledge, and operational debugging capabilities.

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.

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.

Leads the research agenda and architectural direction for large-scale AI communication systems across GPUs, DPUs, NICs, and storage. Conducts original research in RDMA, NVLink, and GPUDirect; drives hardware-software co-optimization; integrates networking into AI serving frameworks; publishes findings; influences industry standards; and mentors senior engineers. The role requires translating systems research into production-grade software and defining complex cross-team technical initiatives.

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.

The role involves developing AI applications for VLSI design using machine learning and related technologies. Duties include infrastructure design, AI solution implementation, collaboration with teams, and maintaining design databases.

Architect and demonstrate simulation workflows using DSX Sim and Omniverse libraries to help partners and customers build, validate, optimize, and operationalize AI factory digital twins. Drive integration of Omniverse libraries, develop reference architectures, prototypes, benchmarks, and sample workflows, and coordinate with sales, product engineering, and partners to resolve blockers and advance adoption.