NVIDIA

Full-stack Engineer

• Define, design, develop and verify features for GPU simulation platforms; collaborating with hardware engineers and fellow software engineers.
• Improve simulation performance, fix bugs across user and kernel stacks, and automate systems for regression testing.
• Heavily involve with the early modeling and simulation required to produce our world-class products.
• Collaborate and communicate effectively with teams from all around the globe.

System Software Engineer, GPU Simulation

Technical Program Manager

• Manage the networking software programs for Nvidia next generation AI Data centers
• Responsible to coordinate between all project stakeholders such as marketing, engineering teams in IL and around the world, operations, etc. from initial requirements definition through Architectural stage, execution, and delivery
• Develop and execute feature planning and prioritization of perception capabilities to meet the software programs' needs
• Identify risks, gaps, and bottlenecks in time, and find resolution with technical leaders and project management
• Work with product managers, architects, and engineers to ensure consistency with company strategy, commitments, and goals

Senior Software Technical Program Manager

Research Scientist

• Develop new Deep Learning models for automatic speech recognition, speech synthesis, neural machine translation and natural language
• Design new large scale training algorithm
• Open-source models using NeMo conversational AI frameworks
• Mentor interns
• Publish research papers on top speech and NLP conferences
• Collaborate with universities and research teams.

Senior Research Scientist

• You'll be part of a small and dynamic team that explores innovative ways to make GPU accelerated applications easier to develop, deploy, and monitor
• Work on enabling GPUs as first-class resources in container orchestrators such as Kubernetes
• Join the core group working on Cloud Native technologies enabling NVIDIA accelerators in the k8s environment
• Work with engineering teams across all of NVIDIA to ensure your software integrates seamlessly up and down the stack
• Proficiently multitask on different tracks to efficiently address evolving priorities

Senior Systems Software Engineer, Containers, Kubernetes

Systems Engineer

• Architect, design and deliver robust implementable solutions for complex supply chain requirements using SAP IBP Supply Planning, Demand Planning, Optimization and Fulfillment Techniques involving SAP IBP utilizing both order-series and time-series planning.
• Demonstrate process improvement techniques to streamline business process and architect solutions.
• Lead projects to implement new and improved existing functionalities that are already implemented at NVIDIA in IBP.
• Collaborate with cross functional teams in business and IT to deliver comprehensive supply chain planning solutions, and Influence senior leadership in business to adopt scalable solutions and systems/tools.

Senior Business Systems Analyst, SAP IBP Planning

Operations

• Coordinate the development of High Performance Computing (HPC) clusters, collaborating closely with internal and external engineering teams.
• Direct and improve GPU capacity and additional compute resources across diverse cloud service platforms to satisfy rising needs and secure efficient deployment.
• Design, improve, and manage data models, reporting platforms, data automation solutions, dashboards, and performance measures that back NVIDIA Infrastructure governance programs and strategic capacity decisions.
• Assess the technical and business requirements for GPU capacity and other compute resources from different internal and external groups.
• Identify performance bottlenecks in day-to-day usage of compute resources and collaborate with relevant infrastructure teams to resolve them.
• Drive infrastructure resource efficiency initiatives in partnership with engineering, finance, and product teams.
• Develop and enhance tooling for our cloud infrastructure and analytics platform to optimize resource usage and performance for NVIDIA and its customers.
• This includes crafting and developing tools for automating workflows and potentially bringing to bear AI techniques to extract useful signals and insights from generated data.
• Partner and cross-collaborate with Finance, Product, Service Owners, and Infrastructure Engineering teams to align cloud capacity management with company goals and develop Infrastructure and Service Level benchmarks to match Customer satisfaction.

Capacity Operations Manager

Program Manager

• Lead cross-functional programs to operationalize new products, services, and service offerings, ensuring readiness across fulfillment, logistics, field service, and support.
• Design and optimize end-to-end fulfillment flows: order intake, kitting, staging, shipping, installation scheduling, returns, repair, and reverse logistics.
• Manage relationships with 3PLs, carriers, depot/repair partners, warehousing teams, and field service providers; negotiate contracts and measure performance.
• Define program benchmarks (OTD, SLA compliance, RMA turnaround, inventory turns, shipping cost per unit, fill rate, first-time fix rate) and build dashboards for leadership.
• Lead continuous improvement initiatives (Lean, Six Sigma tools) to reduce cost, cycle time, and failures while increasing capacity and quality.
• Develop capacity planning and inventory strategies based on projected demand to support product launches and seasonal demand spikes.
• Lead change management: rollout of new systems (WMS, TMS, FSM, ERP integrations), SOPs, training, and governance.
• Own risk management and business continuity planning for logistics and field operations (disruption mitigation, contingency carriers, spare pools).
• Coordinate cross-functional collaborators (product, engineering, sales, finance, customer success) to align service SLAs with commercial commitments.

Logistics Execution Program Manager

Machine Learning Engineer

• Collaborate closely with our partners and the open-source community to deliver their flagship models as highly optimized NVIDIA Inference Microservices (NIM).
• Research and develop innovative deep learning methodologies to accurately evaluate new model families across diverse domains.
• Analyze, influence, and enhance AI/DL libraries, frameworks, and APIs, ensuring consistency with the best engineering practices.
• Research, prototype, and build robust tools and infrastructure pipelines to support our ground-breaking AI initiatives.

Deep Learning Engineer, LLM Accuracy Evaluation

Field Engineer

• Drive the development and execution of functional, regression, and stress testing for networking and GPU technologies within OEM server environments.
• Lead hands-on debugging, firmware validation, and isolation of complex system-level issues for new server generations and fast-release programs.
• Integrate various hardware and software components into an overall architecture to automate end-to-end testing scenarios for efficient packaging and deployment.
• Act as a technical lead in weekly issue scrubs to resolve manufacturing blockers, hardware diagnostics failures, and customer partner concerns.
• Provide onsite technical support to resolve hardware/software problems and maintain lab systems to ensure platform readiness.

Networking Field Applications Engineer

• Designing end2end solutions for Perception and AV stack to enable road network detections across various driving environments from complex intersections to rural curvy roads to multi-level highways.
• Applied research and development of innovative deep learning models for lane graph construction, road boundary detection, traffic element recognition, and other static-world tasks.
• Develop generalizable approaches to support diverse ODDs and Country/region expansion
• Drive and prioritize data-driven development by working with large data collection and labeling teams to bring in high value data to improve perception system accuracy.
• Productize the developed perception solutions by meeting product requirements for safety, latency, and SW robustness.

Senior Machine Learning Engineer, Perception – Autonomous Driving

• Design and train innovative large-scale models—including generative, imitation, and reinforcement learning—to improve the planning and reasoning capabilities of our driving systems.
• Build, pre-train, and fine-tune LLM/VLM/VLA systems for deployment in real-world autonomous driving and robotics applications.
• Explore novel data generation and collection strategies to improve diversity and quality of training datasets.
• Collaborate with cross-functional teams to deploy AI models in production environments, ensuring performance, safety, and reliability standards are met.
• Integrate machine learning models directly with vehicle firmware to deliver production-quality, safety-critical software.

Principal Deep Learning Engineer – End-To-End Autonomous Driving

Project Manager

• Define the product vision and strategy for internal roadmap and execution platforms, aligning the platform with Hardware Infrastructure’s mission and diverse engineering workflows.
• Evolve the platform to support multiple execution models and frameworks (agile, waterfall, hybrid), creating a consistent framework for planning, tracking, and risk management while accommodating team-specific workflows.
• Drive workflow standardization and guidelines, balancing flexibility with clarity to reduce friction, enable alignment, and improve predictability of program execution.
• Conduct user discovery, mapping, and analysis across teams to identify friction points, workflow gaps, and chances to improve usability and efficiency.
• Define product requirements and success metrics (ex: adoption within target workflows, reduction in manual coordination, alignment of schedules, risk visibility) to guide prioritization and measure impact.
• Collaborate with TPMs, Engineering, and UX teams to ensure the platform evolves to meet strategic goals while remaining technically feasible and operationally scalable.
• Advocate for a user-centered approach within the platform, helping teams adopt tools in a way that improves execution, visibility, and collaboration.
• Enable cross-team alignment and communication, ensuring all participants understand and benefit from improvements to the platform.

Product Manager, Internal Project Management Platforms

Software Engineer

• Build, mentor, and scale a team of engineers passionate about hardware platform enablement
• Partner with internal teams to complete bring-up and readiness for next-generation platforms
• Coordinate requirements and schedules with external partners
• Translate evolving roadmaps into clear achievements and measurable outcomes
• Improve automation and operational tooling to streamline onboarding and reduce time-to-production

Senior Director, EDA Engineering

Engineer

• Modeling of compute, and fabric architecture, switch components and other features
• Work in a matrixed environment, across the different modeling teams, to document, design, develop tools to analyze and simulate, validate and verify models
• Engaging closely with customers as we work together to develop new products on our platforms.
• Develop tests, test plans and testing infrastructure for new architectures/ features
• Guide the improvement of the simulation platform and expand other resources to support future architectures
• Develop or use AI to help with day-to-day tasks

Senior Functional Modeling Engineer, NVLink Fusion

Architect

• Define SoC architecture for low-power platforms — CPU, GPU, memory subsystem, low-power IOs, and power management
• Drive concept-level PPA trade-off analysis and lead the power/performance budget across the SoC
• Architect the memory subsystem (LPDDR5/6, on-chip SRAM, bandwidth, QoS) for constrained power envelopes
• Work with DL teams for evaluating and crafting workloads for low power — mapping tasks to constrained hardware; define optimization opportunities in partnership with Nvidia’s DL team.
• Partner with CPU, GPU, and IO architecture teams inside NVIDIA to align on shared IPs and integration strategies
• Translate customer requirements into architectural constraints; drive PRD development and requirements triage
• Engage directly with customers pre-silicon to collect feedback and follow through on product decisions