Top Tech Jobs & Startup Jobs

Design and develop RTOS and hypervisor features, bring up and optimize software stacks on NVIDIA Tegra SoCs, and debug system-level issues across hardware, firmware, RTOS, and virtualization layers. The role develops high-integrity automotive software using safety and security practices, while leading cross-team technical initiatives and coordinating execution. Candidates need strong C/C++ systems programming, embedded software expertise, operating-system knowledge, hardware-assisted virtualization experience, and at least eight years of experience.

Owns multi-generation system power and performance architecture for datacenter and high-performance platforms. Defines HSIO power management, DVFS, P-state, low-power, boot, initialization, reset, and recovery strategies across silicon, firmware, ASIC, board, and platform teams. Drives rack-scale power telemetry and platform coordination, diagnoses system-level performance and power issues, and influences roadmap and product decisions through silicon correlation and release. The role requires deep technical ownership, cross-functional authority, and experience delivering architectures across multiple silicon generations.

Design, implement, and test distributed block and file storage services in C across Linux kernel, userspace, host, and DPU environments. Analyze production issues, resolve complex kernel and systems bugs, develop scalable storage solutions, and collaborate with networking teams, architects, and customers. The role involves defining software architecture for highly performant, scalable storage platforms and requires deep expertise in Linux, storage protocols, distributed systems, and operating-system internals.

Deploys and validates complete AI infrastructure software stacks on multi-node GPU systems, then converts implementations into technical documentation, automation, demos, training, and reference architectures. Tests prerelease software, evaluates interoperability and operational resilience, supports partners and field teams, collaborates with engineering and open-source communities, and recommends product improvements based on customer feedback. The role presents solutions through briefings, workshops, webinars, events, and internal training, with some travel required.

Lead distributed engineering and data teams developing autonomous-vehicle data curation systems and algorithms. Architect in-car data strategies supporting AI training, simulation, and continuous performance improvement. Develop algorithms to mine and classify valuable driving scenarios, establish data quality and metadata standards, and coordinate alignment across research, engineering, and product leadership. The role requires extensive industry experience, technical leadership, autonomous vehicle or robotics expertise, and familiarity with multimodal AI systems.

Develop and maintain high-performance system software and middleware for autonomous vehicle platforms. Design scalable applications interacting with vehicle sensors and heterogeneous architectures, collaborate with hardware, embedded, and cloud teams, and troubleshoot stability, performance, and power issues. Ensure systems meet ISO 26262 functional safety and security requirements while optimizing CPU, GPU, memory, and real-time performance.

Lead NVIDIA’s RL post-training frameworks strategy and engineering ecosystem across distributed training, inference, rollout, evaluation, orchestration, and NVIDIA platforms. Build and manage globally distributed teams, prioritize upstream and internal investments, establish benchmarks and execution metrics, and drive reliable open-source integrations. Partner with research, product, hardware, CUDA, networking, and external communities to scale reinforcement learning workloads across GPUs and heterogeneous systems.

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 architecture and development of enterprise infrastructure automation and configuration management platforms across compute, storage, networking, data centers, cloud, and hybrid environments. Defines long-term technical strategy, builds secure and scalable integrations, applies AI to configuration intelligence and remediation, and remains hands-on with software development. Mentors senior engineers, establishes engineering standards, drives enterprise adoption, and partners with leadership on technical roadmaps and business outcomes.

Lead technical program management for NVIDIA GPU software, coordinating chipset and product roadmaps with software capacity. Manage cross-functional dependencies, prioritization, planning gaps, hardware and release-planning partnerships, process improvements, and execution metrics. The role requires delivering software products while balancing short-term critical timelines with long-term improvements to planning, processes, and team capabilities.