Top Tech Jobs & Startup Jobs

Architect and implement low-latency sensor-to-GPU data pipelines, develop system software, drivers, and APIs, work across FPGA, networking, and CUDA stacks, integrate diverse sensors into Holoscan, and debug and optimize real-time distributed systems under strict latency and throughput constraints while collaborating with hardware, firmware, and AI teams.

Lead NVIDIA's engagement with public-sector wireless research to advance 6G and AI-native RAN projects. Build partner relationships, represent NVIDIA in standards bodies (ATIS, AI-RAN, 3GPP), collaborate cross-functionally, analyze RAN challenges, and drive technical strategies and deliverables aligned with NVIDIA's platform and quality standards.

Lead full lifecycle of L4 Robotaxi carline programs, build and manage a carline PM team, coordinate software/hardware/vehicle/validation integration, drive ML/data ops and in-car testing, resolve cross-functional issues, and manage customer relationships through productization to mass-production readiness.

Design, develop, and productize robust multi-frame, multi-sensor tracking and fusion algorithms for obstacle perception. Improve accuracy, robustness, and efficiency while meeting product safety, latency, and software robustness requirements. Define performance metrics and collaborate with data and ground-truth teams to validate and iterate algorithms at scale.

Lead functional and automated testing of NVIDIA-Cumulus Linux on Mellanox Spectrum switches. Create test plans and topologies, execute tests, develop automation, report and validate defects, and collaborate with design and engineering teams to ensure continuous functional regression coverage.

Lead global product strategy and lifecycle for large-scale network orchestration, telemetry, and analytics products. Define technical use cases and architecture requirements, guide cross-functional teams, engage customers and partners, and deliver scalable datacenter networking solutions aligned with NVIDIAs AI infrastructure roadmap.

Design and implement large-scale automated test infrastructure and orchestration for confidential computing on NVIDIA GPUs. Develop test plans, automate test execution, improve coverage, collaborate across teams, and validate CUDA/driver features in distributed, heterogeneous environments using AI-assisted tools.

Lead a hands-on engineering team to ship production-ready LLM inference via NVIDIA Inference Microservices. Own model onboarding, serving integration, performance optimization, release quality, security readiness, automation, observability, and operational health while partnering with product, research, security, and ops.

Lead design, implementation, and automation of large-scale cloud and data center test infrastructure. Develop CI/CD pipelines, validate performance, scalability, and reliability, debug clusters (network, storage, security), manage Kubernetes and cloud environments, leverage AI tools to accelerate testing, and coordinate cross-team bring-up and issue resolution.

Drive developer enablement for CUDA-Q by creating docs, examples, tooling, MCP servers, Agent Skills, prompt templates, and benchmarks. Analyze developer and agent workflows, define documentation and agent-consumable standards, run evaluations and metrics, and translate research into practical guidance to improve developer time-to-value for quantum and GPU-accelerated applications.