NVIDIA

Account Manager

• Be responsible for all aspects of strategy creation and implementation, forecasting, sales account management, training and education to a select number of enterprise customers
• Be the key point of contact and relationship owner for a defined set of insurance customers
• Grow NVIDIA presence across data science, line of business, and IT organizations
• Build key accounts into strategic partners, and drive sustaining revenue, market share growth and product footprint
• Work closely with partners, OEMs, and ISV ecosystem partners to build and execute go-to-market plans to accelerate growth and adoption of the enterprise product family
• Build winning capabilities and lead change
• Contribute to the long term success of our team by being a collaborative leader amongst peers
• Consistently strive to improve and reinvent yourself
• Evangelize the use of NVIDIA's accelerated computing platform to customers, prospects, and partners
• Ability to travel 25%

Senior Account Manager, Financial Services – Insurance

Solutions Engineer

• Localize the future: Fine-tune LLMs to speak the authentic language of specific regions and industries.
• Develop and optimize training and inference workflows with partners and collaborate with internal NVIDIA development teams to improve our software stack.
• Build sophisticated agentic systems featuring multi-agent coordination, long-horizon reasoning, and sophisticated planning frameworks.
• Develop full-scale solutions, including domain-specific enterprise agents and high-performance retrieval pipelines (RAG) spanning various data sources.
• Optimize inference performance by bringing to bear GPU-accelerated frameworks and the full NVIDIA AI infrastructure stack.
• Build hands-on PoCs and reference architectures that serve as the blueprint for production-grade generative AI pipelines.
• Partner with high-growth startups and Enterprise ISVs to embed NVIDIA’s software stack into their core platforms, slashing the time to market for production-grade AI.
• Fuel partner innovation through hands-on developer enablement and thorough architectural reviews, turning sophisticated AI visions into production realities.
• Scale global expertise by crafting reusable assets and documentation that help field teams deploy agentic AI at scale.

Solutions Architect, Model Builder

Data Scientist

• Own the planning, execution, and successful delivery of data center construction projects, ensuring all project goals and landmarks are met.
• Work closely with a complementary team of architects, engineers, contractors, and internal partners to coordinate all project activities and ensure alignment on objectives and timelines.
• Develop comprehensive project plans that outline scope, objectives, timelines, landmarks, and deliverables, ensuring all aspects of the construction fit out are covered.
• Monitor project progress continuously, identify potential risks, and develop and implement effective mitigation strategies to address any issues that arise.
• Coordinate project budgets, ensuring resources are allocated efficiently and costs are controlled during the project lifecycle.
• Ensure all construction activities comply with relevant building codes, safety standards, and regulatory requirements, maintaining the highest safety standards on site.
• Provide regular updates to partners through status reports and presentations, ensuring transparency and effective communication throughout the project.

Technical Project Manager – Data Center Construction

Network Engineer

• Analyze and model NVIDIA’s AI and High Performance Computing offerings, deep dive into network behavior for training and inference and generate insights to directly impact the architecture of next-generation of AI systems
• Conduct advanced network modeling, research and optimization using innovative simulation tools
• Write production-quality C++ and Python code daily
• Hands-on modeling of advanced network topologies, configurations and traffic patterns representing real life AI workloads, and analyze their effect on the system's performance
• Actively develop the simulator software solution while contributing modular and scalable code
• Improve the quality of the simulation as a software product, ensuring robustness and reliability

Senior Network Modeling Architect

Cloud Engineer

• Collaborate with the network architecture team to review and comment on network designs
• Lead the deployment, management, and troubleshooting of firewall, load balancing, and IDS/IPS solutions
• Evolve security feature set using telemetry and advanced analytics
• Define and maintain comprehensive security guidelines for deployments
• Orchestrate rigorous vulnerability management, including patching and bug scrubs
• Design Identity and Access Management (IAM) policies for services
• Serve as a key stakeholder in security reviews and ACL approvals
• Lead incident response efforts by investigating security alerts
• Partner with the global operations team to ensure necessary operational tools

Senior Network Security Engineer – DGX Cloud

Planner

• Drive Manufacturing Partners to close execution gaps and meet AVC commitments.
• Manage daily build plans and factory trackers to ensure production schedules remain on target.
• Drive resolution of material shortages impacting production and customer deliveries.
• Identify factory constraints and drive trade-off decisions to meet commitments.
• Coordinate rework activities to support customer demand and recovery plans.
• Track and improve factory metrics, including On-Time Delivery (OTD), output attainment, and execution performance.
• Manage production line holds and release instructions to support quality, material, and operational requirements.
• Drive bonepile recovery activities at the factory and minimize shipment impact.
• Support overtime (OT) planning and execution to address capacity and demand requirements.
• Support NVIDIA's manufacturing expansion and business growth initiatives in Mexico.

Senior Factory Planner

Engineer

• Verify our custom analog macros.
• Partner with Design, Integration and TE teams to determine verification scope, develop strategies, implement test planning, and verify designs at IP level, cluster level, and full chip level
• Collaborate with CAD team to optimize & smooth our simulation flow
• Generating test patterns and perform chip bringup

DFT Verification Engineer

• Own the customer relationship end-to-end
• Build trusted, long-term partnerships at C-level across engineering, IT, manufacturing, and corporate strategy teams
• Understand each account's AI roadmap and position NVIDIA technology and partners as the strategic choice
• Identify and close transformational opportunities
• Working closely with NVIDIA Solution Architects, define, develop, and close deals for production-scale deployment of the NVIDIA platform — hardware and software
• Develop and implement account strategy
• Build multi-year account plans that deliver consistent revenue growth
• Map the ecosystem of partners, resellers, and integrators and orchestrate them to enhance impact
• Stay current on the French and broader European automotive AI landscape, competitive dynamics, and emerging use cases

Global Account Manager, Automotive

Business Development Rep

• Serve as the trusted strategic advisor, problem solver, and champion for customers and partners in the Automotive industry and ecosystem.
• Engage with customer and partner business teams, technical leaders, and decision-makers to identify goals, tackle business challenges, and guide them in their AI journeys.
• Foster innovation speed and positive business impact by highlighting the value of groundbreaking technologies in solving real world problems in the Automotive industry.
• Accelerate critical business opportunities.
• Map, track, and monitor the partner ecosystem to identify growth opportunities.
• Collaborate multi-functionally with solution architects, engineering, product management, and marketing to drive engagement.
• Supply actionable insights from field engagements to influence product roadmaps.

Business Development Lead, Automotive

Full-stack Engineer

• Work with a geographically distributed partner organisation to understand, modify and improve CPU Compiler SW at NVIDIA.
• Analyse AI workloads to champion improvements in both new hardware and compiler toolchain and improve hardware-software co-design.
• Contribute new features and optimisation techniques targeting NVIDIA CPUs engaging with upstream and open source communities.
• Develop compiler SW that is optimised for performance.
• Be part of a team that is at the centre of artificial intelligence, high-performance computing and data centre technologies.
• Contribute towards the development of next generation compute

Senior CPU Compiler Performance Engineer

Infrastructure Engineer

• Design, build, and operate the Kubernetes platform that powers GNI network automation, telemetry, and operations across data center, colocation, and cloud environments.
• Own the lifecycle management for GNI Kubernetes environments, including cluster onboarding, upgrades, capacity, availability, and recovery.
• Develop production-quality software and automation for cluster provisioning, validation, upgrades, remediation, and safe multi-cluster delivery through GitOps.
• Provide production support for network services hosted on the platform, working with Network Automation and service teams that retain ownership of application architecture, code, and features.
• Diagnose complex Kubernetes platform and hosted-service failures involving control-plane health, cluster networking, storage, scheduling, workload placement, and multi-cluster dependencies.
• Drive issues from initial signal through verified resolution.
• Define production-readiness and observability standards for the platform and hosted network services, including health signals, capacity, alerts, runbooks, and recovery.
• Participate in CFR’s production on-call rotation, including scheduled after-hours and weekend coverage.
• Lead incident response and recovery, then drive corrective actions to completion.

Senior Platform Engineer, Network Infrastructure

• Work across the full stack of a distributed systems platform, from crafting gRPC contracts to building secure sandbox runtimes.
• Implement and harden network security features, including policy enforcement, L4/L7 proxies, and secure inter-service communication using mTLS.
• Develop core platform components such as inference routing, ensuring model provider adapters, credential management, and protocol translation integrate seamlessly with the sandbox and gateway.
• Build reliable configuration and control plane systems that handle state divergence, implement reconciliation loops, and support safe merging and hot-reloading policies.
• Own the operability experience by creating effective CLI tools, managing release automation, and instrumenting all systems for observability with structured logging and distributed tracing.

Software Engineer, OpenShell

• Apply knowledge of compute programming models and compute architecture to build tools that provide actionable feedback to compute developers
• Work in existing driver code and application code as well as writing new shared libraries and targeted performance tests
• Develop the Compute Sanitizer (which is a suite of memory checker) tools for GPUs running on Linux, Windows, and embedded operating systems
• Work with tools, compiler, architecture and driver teams to design, implement and verify new features in the Compute Sanitizer stack
• Collaborate with internal and external partners including other peer organizations within NVIDIA
• Effectively estimate and prioritize tasks in order to create a realistic delivery schedule
• Write fast, effective, maintainable, reliable and well-documented code
• Provide peer reviews to other engineers, including feedback on performance, scalability and correctness
• Document requirements and designs, and review documents with teams throughout NVIDIA
• Mentor junior engineers

Senior Software Engineer, Compute Sanitizer – GPU

AI Engineer

• Design, develop, and improve scalable infrastructure to support the next generation of AI applications
• Drive improvements in architecture, performance, and reliability
• Collaborate across hardware, software, and research teams
• Stay informed of the latest advancements in AI infrastructure

Senior Applied AI Engineer

Systems Engineer

• Work on end‑to‑end performance and scalability analysis across the Kubernetes‑based accelerated runtime stack (control and data planes), including NVIDIA components such as GPU Operator, Network Operator, node-feature-discovery, topograph, dra-driver-nvidia-gpu, and nvsentinel, tracking issues from orchestration down to the metal.
• Design and contribute upstream architectural changes to the Kubernetes control plane and related projects to enable reliable operation at hyperscale cluster sizes, doing in the open what today’s hyperscalers typically do privately.
• Improve container startup and cold‑start latency to enable smooth, low‑latency inference scaling on Kubernetes across thousands of GPU nodes, ensuring the AI runtime stack scales without creating API server pressure or operational fragility.
• Assess, improve, and contribute to open‑source projects that make Kubernetes an outstanding platform for AI workloads (for example, Grove and gateway-api‑inference‑extension), composing their architectures with scalability, resilience, and multi‑node training/inference in mind.
• Advance scalability and performance of confidential containers (CoCo) on Kubernetes so encrypted inference workloads meet stringent efficiency and latency requirements in production.
• Use DSX and related large‑scale simulation infrastructure to model full AI‑factory deployments and validate scalability across thousands of simulated GPUs, catching failures that emerge only at scale before hardware arrives.
• Collaborate with AI researchers, developers, customers, and upstream communities to design automated, at‑scale workload tests (including replay of production agent traces), build monitoring/analysis tooling, and integrate continuous performance and scale testing into modern CI/CD workflows.
• Document methods and results clearly and present findings internally and at industry events (for example, KubeCon, GTC), while actively engaging with upstream groups (Kubernetes SIG Scalability, CNCF, and NVIDIA OSS communities) to influence and validate AI workload performance and scalability directions.