NVIDIA

Role Description As a Senior Quantum Algorithm Researcher, you will help grow the adoption of our products and collaborate with Product, Engineering, and Research teams to drive innovation at the intersection of quantum computing applications and AI. Your work will contribute to NVIDIA quantum product in key areas such as: - AI methods for quantum algorithm discovery - Advanced quantum simulations - Digital twins NVIDIA’s Quantum group is a small, high-impact team visible both internally within NVIDIA and externally across the global quantum community. We’re looking for passionate quantum algorithm researchers who want to help define the future of the field. What You'll Be Doing: - Establish and lead technical collaborations with supercomputing centers (SCCs) on hybrid quantum-classical application development across multiple industry verticals. - Lead research pathfinding in AI for quantum algorithms. - Drive the execution of technical results for projects in collaboration with our strategic partnerships, ensuring alignment with strategic objectives and customer success. - Contribute to NVIDIA’s revenue stream through strategic technical engagement and the development of innovative solutions that support customer adoption and long-term partnerships. - Publish impactful research works and represent NVIDIA at relevant scientific, technical, and industry conferences. Qualifications - Ph.D. in Quantum Computing, Physics, Computer Science, or a related field (or equivalent experience). - Over 9 years of experience working on Quantum Algorithms and Applications. - Demonstrated leadership in technical collaborations with industry partners. - Excellent programming skills in Python, C++, and a parallel programming model (e.g., CUDA). - World-class communication and interpersonal skills with a proven ability to articulate a value proposition to technical and non-technical audiences. - A track record of research excellence demonstrated in publications and presentations at leading conferences. Requirements - Recognized thought-leadership in the cross section of quantum applied research and high-performance computing. - Experience with the NVIDIA Quantum platform (cuQuantum or CUDA-Q).

• Be an inspiring leader on integrating NVIDIA technology into IT infrastructures • Interact with end-users in academia and industry • Identify gaps and propose/develop prototypical solutions • Demonstrate accelerated computing and AI workflows • Communicate customer requirements to NVIDIA Engineering

• Empower and mentor partners, customers, and colleagues, sharing your expertise to help them grow and succeed. • Play a key role in shaping and supporting strategic plans that drive innovation at national, regional, and city levels across diverse communities. • Build engaging resources—such as presentations, blogs, and interactive notebooks—that connect business strategy with real-world government and community needs. • Collaborate closely with customers’ technical teams, guiding and supporting them through hands-on field trials and solution deployments. • Advocate for customer and partner needs by helping define product requirements and championing enhancements that remove barriers. • Develop and demonstrate solutions based on NVIDIA’s groundbreaking AI software and hardware technologies to customers.

• Own the full lifecycle of GPU compute clusters — procurement, provisioning, configuration management, monitoring, and deprecation — across heterogeneous Linux environments (DGX, HGX, embedded systems) • Design and scale storage solutions (NFS, Lustre, WekaFS, or equivalent) with a clear roadmap for capacity and performance growth • Lead automation of infrastructure using modern IaC tools (Ansible, Terraform) and CI/CD pipelines (GitLab) • Manage and optimize job scheduling via Slurm, including fair-share policies, reservation management, and MIG/GPU partitioning strategies • Maintain and improve observability stacks (Prometheus, Grafana, DCGM) and drive proactive resolution of hardware and software incidents • Collaborate with ML engineers and software teams to tune cluster configuration for large-scale distributed training workloads • Evaluate and introduce new technologies — networking fabrics (InfiniBand, NVLink, EFA/RDMA), storage tiers, container runtimes — to improve performance and reliability • Mentor junior engineers and contribute to team-wide engineering standards

• Design, implement, and maintain CI/CD pipelines for building OS images across Ubuntu and RHEL distributions • Build unit testing and integration testing frameworks using Go and Python • Optimize CI/CD infrastructure using Jenkins and GitlabCI • Implement infrastructure as code with Terraform and work with Kubevirt for virtualized workloads • Create and maintain Ubuntu and RHEL packaging solutions (deb/rpm) • Identify infrastructure improvements and implement metrics gathering systems for KPIs and dashboards • Champion automation initiatives to achieve 100% end-to-end automation across developer and release workflows

• Build and implement critical features for core NGC services • Play a key role in developing the unified portal for NVIDIA cloud products • Proactively apply AI tools and agents to boost code quality and system stability • Drive the evolution of application architecture and development patterns • Work closely with Product, UX, and Backend teams to create user-friendly UIs • Participate in all stages of the product lifecycle • Estimate personal timelines and coordinate work • Present and/or demonstrate your work/research

• Drive end-to-end UX design for developer and creative tools, from discovery through delivery, shaping experiences that simplify complex workflows • Generate deep user insights through contextual inquiry, surveys, focus groups, and direct engagement in beta communities • Translate research findings into intuitive user flows, wireframes, and interactive prototypes, leveraging AI-powered design tools • Lead usability testing efforts to validate concepts and refine both prototypes and implemented experiences • Partner closely with engineering and product teams to ensure design intent is preserved through development • Contribute to scalable, high-quality experiences by maintaining design system consistency and strong usability standards

• Drive platform bringup, feature enablement, end-to-end software validation, and debug for next-generation NVLink-based GPU and rack-scale systems. • Develop tools, diagnostics, automation, and infrastructure for system validation, regression testing, and fleet support. • Lead reliability and MTBI validation through stress testing, telemetry analysis, failure injection, and issue resolution. • Triage complex software, firmware, networking, and platform issues across validation, deployment, and production environments. • Collaborate with architecture, hardware, firmware, software, and Customer engagement teams to improve system quality and reliability. • Build and maintain SRE-style validation infrastructure, including provisioning, monitoring, and operational readiness. • Create automation, dashboards, runbooks, and debug workflows that improve root-cause analysis and operational efficiency.

• Design, build and optimize agentic AI systems for the CUDA ecosystem • Co-design agentic system solutions with software, hardware and algorithm teams; influence and adopt new capabilities as they become available • Develop reproducible, high-fidelity evaluation frameworks covering performance, quality and developer productivity • Collaborate across the AI stack—from hardware through compilers/toolchains, kernels/libraries, frameworks, distributed training, and inference/serving—and with model/agent teams

• Design, implement and maintain large scale HPC/AI clusters with monitoring, logging and alerting • Manage Linux job/workload schedules and orchestration tools • Develop and maintain continuous integration and delivery pipelines • Develop tooling to automate deployment and management of large-scale infrastructure environments, to automate operational monitoring and alerting, and to enable self-service consumption of resources • Deploy monitoring solutions for the servers, network and storage • Perform troubleshooting bottom up from bare metal, operating system, software stack and application level • Being a technical resource, develop, re-define and document standard methodologies to share with internal teams • Support Research & Development activities and engage in POCs/POVs for future improvements