NVIDIA

Technical Program Manager

• Partner with Engineering, Infrastructure, Software teams, and leadership to drive AI capacity enablement and management programs
• Develop and mature foundational capabilities and processes for DGX Cloud
• Manage cluster and capacity bring-up, including CPU, storage, networking, and GPU compute requirements
• Coordinate with storage and network engineering teams to define requirements for Cloud Service Providers and NVIDIA Cloud Providers
• Drive alignment and a plan of record for capacity blocks based on workload needs
• Engage CSP and NCP partners on managed storage and network solutions and align them with NVIDIA's cloud roadmap
• Gather technical requirements, develop roadmaps, establish turning points, and ensure adherence to the Product Lifecycle process
• Manage ongoing capacity operations and engineering engagement with cloud provider partners
• Focus on availability, maintenance, and critical performance indicators
• Understand workload requirements and hardware/infrastructure needs to optimize cloud-vendor readiness
• Use Jira and other program management platforms to structure engineering deliverables
• Drive adoption of third-party and in-house cloud infrastructure solutions for deployment, support, security, compliance, and observability
• Establish KPIs and quantitatively demonstrate program value and impact
• Identify, resolve, and mitigate risks and issues affecting scope, schedule, and quality
• Identify process-improvement opportunities within cloud infrastructure operations

Technical Program Manager – Cloud Infrastructure

• Initiate and lead multiple concurrent technical programs across the quantum computing portfolio, emphasizing accelerated computing.
• Define execution plans and measurable outcomes from R&D and technology roadmaps, and drive work to success across teams.
• Identify and prioritize critical activities required for research success and ensure completion.
• Develop strong technical understanding of R&D priorities to enable collaborations and identify new opportunities.
• Solve and mitigate technical challenges while establishing early risk identification and mitigation plans.
• Link research and engineering, moving technologies from incubation to scalable solutions.
• Partner across engineering, applied research, and product teams inside NVIDIA and with external collaborators.
• Build and operationalize innovation roadmaps for quantum computing.

Senior Technical Program Manager, Quantum Computing Applied Research

• Define strategy, governance, operational performance indicators, success metrics, and execution models
• Scale NVIDIA Tech Days for universities as a repeatable global program, including institution prioritization, content development, execution standards, measurement, and continuous improvement
• Support curriculum integration collaborations with leading universities and faculty advocates
• Coordinate strategic academic investments, including operations for university lab sponsorships and NVIDIA’s education rebate program
• Collaborate with NVIDIA field staff, including Developer Relations, Account Managers, and Solution Architects, to ensure consistent global program execution
• Collaborate internally with NVIDIA Developer Programs, Deep Learning Institute, and Product teams to support leading universities
• Synthesize insights from faculty, researchers, and students to help build future NVIDIA platforms, libraries, and educational content
• Track and communicate outcomes, including program reach, library adoption, open-source contributions, paper citations, and infrastructure utilization
• Identify ecosystem gaps, test new program concepts, and scale successful pilots across regions and research domains

Technical Program Manager – Higher Education, Research

Account Manager

• Develop deep technical understanding of NVIDIA AI and accelerated computing portfolio, including GPU, DPU, CPU, networking, and reference architectures
• Become an authority on Data Center, Client, Edge, Networking, and Storage products to identify new solution opportunities for NVIDIA’s full stack of products
• Describe NVIDIA’s value proposition, product offerings, and market-leading positioning
• Lead packaging of NVIDIA technology and capabilities into Lenovo solutions
• Build co-market and co-sell strategies for Cloud and Enterprise markets through Direct, VAR, Distribution, and GSI sales channels
• Manage business operations through development team coordination, supply planning, pricing, and quality improvement processes
• Build senior management relationships across development, marketing, and sales
• Collaborate with Lenovo and NVIDIA platform development teams covering hardware, software, delivery and support, product management, product marketing, and GTM teams

Global Account Manager – AI, Accelerated Computing Hardware

• Support Tesla Motors in automated driving, cockpit applications and the automotive ecosystem
• Work closely with NVIDIA solution architects and business development leaders to drive design wins and revenue growth
• Work with OEMs, manufacturers and ISVs in autonomous driving and cockpit electronics
• Evangelize NVIDIA's deep learning-based approach
• Develop strategic relationships with customers' R&D and procurement teams
• Identify new opportunities and drive design wins
• Understand customers' system architecture, existing solutions, development plans, competition status, product strengths and weaknesses, market position, and technical/commercial challenges
• Position NVIDIA effectively for customer opportunities
• Engage and steer Development, Marketing and Operations teams to provide customer service and drive business growth
• Identify vehicle platforms and key personnel at Tier 1 customers related to NVIDIA automotive products

Automotive Account Manager

Application Engineer

• Develop new tools and features in NVIDIA ALCHEMI for performant atomistic simulation workflows
• Guide customers or developers integrating material science applications with AI surrogates using NVIDIA ALCHEMI
• Profile applications and run benchmarks to demonstrate product value
• Collaborate with subject matter experts on whitepapers and research
• Participate in workshops focused on material science and AI
• Work across research, engineering, and product teams to build software tools bridging traditional simulations and AI surrogates

Senior Applications Engineer, AI for Material Science, Chemistry

Developer Relations

• Drive the architecture and implementation of math libraries APIs and documentation structures designed to be LLM-first: introspectable, explainable, and easily synthesized by AI agents
• Work with internal and external stakeholders to deliver timely LLM-enhanced library releases
• Build metrics, tools and processes to measure impact and quality of LLM/agentic code generation
• Prototype tooling and solutions to help transform math libraries’ APIs into LLM-friendly representations across popular codegen tools

Senior Math Libraries Engineer – LLM Integration, Developer Experience

Full-stack Engineer

• Develop the next-gen Scale-up fabric for AI data-centers
• Concentrate on collaborative communication and vertical co-development of AI networks alongside hardware, software, and applications
• Guarantee scalability, reliability, and robust training for large model development
• Collaborate with cross-functional teams to build and implement innovative solutions
• Create innovative concepts that will ultimately proceed to full-scale manufacturing
• Partner with world-class talent to successfully implement new technologies

Senior Software Architect, Networking AI

Security Engineer

• Participate in architecture development and document specifications
• Work with RTL and the verification team to ensure that the design meets the specification
• Help deliver a design that meets NVIDIA's and its customers' high security standards
• Work closely with NVIDIA security architects, design engineers, verification engineers, and software engineers
• Help lead architecture work on security engines and secure boot flows

Senior Hardware Security Architect

DevOps Engineer

• Build, implement and support operational and reliability aspects of large-scale Kubernetes clusters with focus on performance at scale, real-time monitoring, logging, and alerting
• Define SLOs/SLIs, monitor error allowances, and streamline reporting
• Support services before launch through system creation consulting, developing software tools, platforms and frameworks, capacity management, and launch reviews
• Maintain live services by measuring and supervising availability, latency, and overall system health
• Operate and optimize GPU workloads across AWS, GCP, Azure, OCI, and private clouds
• Scale systems sustainably through automation and drive changes that improve reliability and velocity
• Lead triage and root-cause analysis of high-severity incidents
• Practice balanced incident response and blameless postmortems
• Participate in on-call rotation to support production services

Senior Site Reliability Engineer, DGX Cloud

Implementation Specialist

• Build and develop leaders running a globally distributed customer engineering team
• Own the reactive support queue for EDA infrastructure and escalate into service engineering with high-quality signal
• Run the Kubernetes platform team as a customer engineering service, including availability, roadmap, developer experience, and operational excellence
• Own the evolution of the AI support agent from FAQ bot to a system that safely resolves issues against real internal systems
• Define and defend SLOs for operational performance and secure stakeholder buy-in
• Drive deep support and continuous improvement across EDA job schedulers, Kubernetes platforms, and internal LLM harnesses
• Design software solutions and automation pipelines to eliminate operational overhead
• Stay connected to live production environments and engineer permanent automated remedies for recurring toil

Head of Global Customer Engineering – Internal EDA Infrastructure

Network Engineer

• Own and lead the Hardware Performance and Qualification tooling suite
• Drive continuous improvement in test coverage, automation, and accuracy with ODMs and OEMs
• Drive the design-in process with top-tier ODM and OEM partners
• Ensure seamless hardware integration and best-in-class platform experiences
• Collaborate with multiple R&D departments to identify, escalate, and resolve hardware issues rapidly
• Define and execute qualification plans for new hardware platforms
• Validate performance, stability, and compliance across diverse system configurations
• Build and nurture technical relationships with ODM/OEM engineering teams
• Act as the go-to expert for hardware performance and qualification
• Provide actionable feedback to engineering and product teams based on qualification findings and partner interactions

Senior Hardware Network Engineer

Business Development Rep

• Identify potential NVIDIA Embedded technology partners and opportunities to collaborate
• Identify complementary technology partners needed to build complex solutions using embedded computing edge AI platforms
• Identify key customers and understand their goals, strategies, and technical needs
• Define and position NVIDIA AI platforms to deliver high-value edge-based AI solutions
• Establish close technical ties with customers and foster relationships to facilitate rapid issue resolution and customer satisfaction
• Work with NVIDIA business units and engineering teams to define software and hardware components for winning strategies
• Develop a rich ecosystem of partners and support customers and partners in achieving product goals
• Liaise with alliance managers to develop marketing partnerships with industry software vendors to drive growth
• Prioritize and report key business metrics for global industry teams
• Advise, train, and present to internal and external customer teams regarding requirements and business trends
• Build scale-out plans for target-market technologies
• Provide insight into technology trends and market analysis
• Represent and evangelize NVIDIA solutions at key industry events

Embedded Business Development Manager – Industry

• Build and lead the Infrastructure Security Engineering team for EDA clusters
• Grow and direct a team of engineers and develop leaders within it
• Own the technical roadmap for EDA-specific security controls
• Partner with security teams and infrastructure engineering to architect and ship controls into production
• Use network and platform infrastructure as the security control plane
• Determine where conventional endpoint and workload security tooling fits and design alternatives where needed
• Protect multi-day compute jobs without degrading performance
• Drive consistent enforcement of security frameworks across on-premises data centers and multi-cloud platforms
• Manage the full lifecycle of core infrastructure security technologies, from selection and deployment through operation and replacement
• Build KPIs, instrumentation, and audit workflows to continuously expose security posture
• Plan mitigations across network, storage, and compute
• Embed automation and secure defaults into the infrastructure lifecycle

Head of Infrastructure Security Engineering – EDA Clusters

• Own and evolve the Kubernetes networking architecture for GPU clusters running at multi-thousand-node scale
• Design, operate and scale the overlay network - CNI, mesh and VPN topologies (Tailscale, WireGuard), and the gateways that connect control and data planes
• Design, operate and scale the L7 gateways/load balancers/tunnels (Envoy, Cloudflare)
• Find and eliminate scale ceilings: packet loss under load, control-plane saturation, IP address management exhaustion, and failure modes above a few thousand nodes
• Build scale-test environments and validation suites to catch networking regressions before production
• Diagnose hard, ambiguous problems across the stack, tracing symptoms in Slurm or training jobs to networking root causes
• Partner with cloud and neocloud providers on network topology, requirements and capabilities for new clusters
• Provide senior technical judgement to a distributed team in the Cluster Networking domain