Software Engineer Jobs in San Jose, CA

Lead technical projects for product scaling. Design and maintain APIs and services, ensuring reliability and user experience while collaborating with product teams.

Design and implement highly reliable, high-performance algorithms and systems software for modern data platforms. Own end-to-end delivery from concept to production, diagnose and resolve complex technical issues, influence cross-functional system design, and mentor junior engineers. Drive adherence to performance, reliability (e.g., six 9s), schedules, and architectural vision while collaborating with product and engineering teams.

As a Senior/Lead Software Engineer, you'll design and build network systems features, manage development processes, and collaborate with engineers and customers. Responsibilities include software development in C, C++, Python, and maintaining hardware drivers, while mentoring junior engineers.

Lead design and implementation of agentic workflows, optimization algorithms, and ML/DL solutions for EDA. Research novel ML approaches, develop and maintain software (APIs, C/C++, Python) on Linux, and collaborate with global cross-functional teams. Requires ML/DL framework experience (PyTorch) and LLM/agentic AI understanding.

Lead design and implementation of high-performance distributed storage systems and protocols (object, block, file) for AI infrastructure. Set technical direction, author designs, mentor engineers, integrate with hardware (NVMe, DPUs, GPU-direct), optimize performance, build benchmarking and observability tooling, and drive cross-team deployments and incident response in multi-petabyte data center environments.

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 architecture and optimization of deep learning inference for autonomous driving platforms. Drive end-to-end performance analysis across models, compiler/runtime (TensorRT/Triton), CUDA kernels, memory and scheduling. Develop model optimization techniques (quantization, pruning, fusion), build profiling and benchmarking tools, and collaborate with platform, perception, safety, and silicon teams to deploy efficient, reliable DNNs on NVIDIA automotive compute platforms.

Architect and build production-grade AI applications and shared platform services across desktop, mobile, web, and cloud. Lead full-stack engineering, CI/CD, observability, and operational excellence. Build foundations like agent orchestration, memory/context services, evaluation frameworks, telemetry, and workflow execution. Operationalize NVIDIA AI technologies, collaborate with cross-functional teams, mentor engineers, and drive architectural alignment to deliver scalable, reliable enterprise AI products.

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 and build scalable, production C++ mapping systems using crowdsourced fleet data. Develop 3D reconstruction, multi-view geometry, pose estimation, sensor-fusion, map-fusion, and change-detection algorithms. Create offline pipelines, evaluation metrics, visualization/debugging tools, and integrate maps with perception, localization, and planning systems to improve autonomy safety and coverage.

Lead development of planet-scale mapping systems from crowdsourced fleet data. Design and implement 3D reconstruction, SfM, pose estimation, sensor fusion, map fusion, change-detection, and production C++ pipelines. Build evaluation, visualization, and debugging tools, and collaborate with perception, localization, and planning teams to integrate high-quality maps into autonomous driving systems.

Design, build, and operate cloud-native, large-scale distributed data platforms and high-performance microservices. Focus on system design, scalability, reliability, filesystems/storage protocols, production debugging, and delivering SaaS products on AWS/Azure/GCP in an Agile environment.

Design and optimize high-performance C++/CUDA libraries for GPU-based data processing. Improve multi-node GPU communication (UcxExchange), optimize Presto GPU query performance, and lead enhancements across Presto, Velox, and cuDF projects for efficient DataFrame and database acceleration.

Build and maintain scalable Kubernetes control-plane services, operators, and automation for GPU-accelerated AI workloads. Implement GPU-aware orchestration, integrate high-performance networking (InfiniBand/RDMA/GPUDirect), ensure observability (Prometheus, Grafana, tracing), develop inference platform services, create internal CLIs/tools, and support production systems via on-call rotations.

Lead design and implementation of a highly available GPU/CPU host and instance lifecycle control plane. Drive hardware enablement, BIOS/firmware and DPU integrations, multi-tenant security, and durable execution models. Provide technical leadership across teams, translate customer and datacenter requirements into scalable infrastructure, and set engineering standards for mission-critical, large-scale AI cloud systems.

Lead design and implementation of a highly available GPU/CPU host and instance lifecycle control plane. Bridge firmware, kernel, DPU, and semiconductor architecture with distributed systems. Provide technical leadership, mentor senior engineers, set engineering standards, collaborate with product and data-center teams, and translate customer requirements into scalable infrastructure.

Build and maintain scalable Kubernetes control-plane services, operators, and automation for GPU-aware orchestration. Partner on networking (CNI, InfiniBand/RDMA/GPUDirect), observability, inference platform services, tooling/CLIs, and on-call production support for large-scale AI clusters.

Lead QA activities for security networking products: design and run tests, automate scripts and tools, debug and document issues, collaborate with developers and international teams, and produce training and process guidance to improve product quality and team efficiency.

Design, build, and enhance the FortiSASE platform focusing on scalability, reliability, AI-driven features, telemetry analytics, data persistence, and RESTful APIs. Collaborate with engineering, product, and operations teams to architect solutions, evaluate emerging AI/ML technologies, troubleshoot production issues, and drive MLOps and observability practices.

Design, implement, and maintain scalable cloud-native networking and security software for a next-generation SASE platform. Fix bugs, optimize performance, collaborate with DevOps and QA, and deliver high-quality features for distributed, microservice-based systems.

Lead design and delivery of scalable, secure platform and identity services; build data-platform and security troubleshooting applications; define architecture and standards; mentor senior engineers; partner with product, security, and business teams to enable observability, analytics, automation, and long-term platform strategy.

Build and maintain developer platforms, CI/CD automation, and high-availability solutions. Develop services and tooling in Python/Go/Rust, extend CI frameworks (e.g., Jenkins), apply Linux fundamentals, embed security into pipelines, drive capacity planning, automate vulnerability remediation, and implement observability, incident management, and on-call practices to improve delivery velocity and reliability.

Lead hardware/software bring-up of analog/mixed-signal and sensor ICs on customer MCUs/SoCs. Write driver-level firmware and application demos, debug full signal-chain issues, build reusable driver frameworks, document architectures and bring-up guides, lead technical integrations with partners, mentor engineers, and support customer lab demos and trade-show deadlines.

Design, develop, and operate large-scale SDN control and data plane software (OVS/OVN/OpenFlow) for NVIDIA AI Cloud. Build network orchestration and IaC (gRPC/REST, Ansible, Terraform, ArgoCD/Flux), maintain CI/CD pipelines, implement observability/monitoring, drive upstream open-source contributions, and collaborate with SRE/DevOps to ensure production reliability and performance SLAs.

Lead software safety for deep learning systems in autonomous driving: ensure development processes meet ISO 26262, promote safety culture, influence hardware/software architecture, drive safety activities across concept-to-validation phases, and support internal/external ISO 26262 audits and conformance.