NVIDIA

Solutions Engineer

• Assist independent software vendors, ecosystem partners, and lighthouse customers in assessing and adopting technologies within DSX Sim, and Omniverse libraries.
• Architect and demonstrate simulation workflows that help customers build, validate, optimize, and operationalize AI factory digital twins.
• Collaborate with partners to build and validate simulation-ready asset pipelines using OpenUSD, SimReady specifications, engineering data, multi-physics simulation, and operational inputs.
• Drive incorporation of Omniverse libraries (ovRTX, ovPhysx, ovStorage, usd-agents, etc) into partner applications, services, and enterprise workflows.
• Develop reusable reference architectures, demonstrations, sample workflows, benchmarks, and proof-of-concept evaluations that address customer challenges.
• Translate technical and business requirements into clear solution plans while coordinating with sales, product engineering, developer relations, and partner teams to resolve blockers and advance adoption.
• Gather field insights, advocate for partner needs, and contribute technical mentorship that improves product direction, enablement resources, and team execution.

Solutions Architect, Physical AI, Omniverse

• Partner with ISVs on discovery, architecture reviews, technical deep dives, POCs, benchmarks, demos, and production deployment guidance
• Advise on the design, build-out, and optimization of accelerated AI infrastructure, including large-scale clusters
• Support infrastructure design across compute, networking, storage, containers, observability, security, power, and data center operations
• Drive adoption of systems monitoring, telemetry, and management tools to improve cluster utilization, reliability, performance and workload insight
• Build repeatable reference architectures, deployment guides, sizing guidance, benchmark reports, technical playbooks, demos and whitepapers
• Travel up to 20% customer meetings may be required

Senior Solutions Architect – AI Infrastructure, Enterprise ISVs

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

Data Analyst

• Build and deploy scalable ML/AI and optimization models to enhance demand forecasting, optimize capacity allocation, and develop user-specific feature engineering for real-time cloud gaming services.
• Develop reusable framework deployments for data ingestion, processing, and analysis to support dynamic user interventions for targeted business outcomes.
• Acquire and apply domain knowledge of the product and software stack to identify and drive the resolution of data inconsistencies and improve model performance, especially in the context of optimization outcomes.
• Identify, analyze, and interpret trends or patterns in complex data sets using supervised and unsupervised learning techniques, informing prescriptive solutions.
• Design and implement improvements to real-time prescriptive scheduling pipelines, using techniques like linear programming and constraint optimization, to enhance capacity utilization and user retention.
• Improve productivity of the organization by mining petabytes of data for actionable insights for business and engineering, often through prescriptive recommendations.
• Collaborate with a variety of partners to understand requirements, design robust solutions, and guide the team to deliver impactful results.
• Leverage agentic AI to deliver best-in-class automation and programming solutions for complex analytical problems.

Senior Data Scientist, Cloud Gaming – Prescriptive Analytics and Optimization

Architect

• Analyze state of the art DL networks (LLM etc.)
• Identify and prototype performance opportunities to influence SW and Architecture team for NVIDIA's current and next gen inference products
• Develop analytical models for the state of the art deep learning networks and algorithm
• Specify hardware/software configurations and metrics to analyze performance, power, and accuracy
• Collaborate across the company to guide the direction of next-gen deep learning HW/SW

Deep Learning Performance Architect

Hardware Engineer

• Engage in next-generation SmartNIC firmware develop
• Validate the functionalities of firmware and silicon design before tape-out, bring up new silicon when tape-out is done
• Implement new features for congestion control, virtualization, QoS, etc.
• Support local customers to resolve issues in deployment
• Supply creative ideas to improve products and efficiency

Firmware Engineer

• Document and demonstrate advancements in accelerated computing & AI applications through targeted trainings, sharing sessions, whitepapers, blogs, and wiki articles.
• Realize our vision by introducing NVIDIA technology in telecommunication applications for management and operation, and for cloud and edge computing (NFV).
• Develop a keen understanding of customer’s goals and needs, deliver high-value solutions, and support technical trials, proof-of-concepts and customer adoption.
• Lead technical project aspects of complex deployments, including design-in opportunities and responding to RFP/RFI proposals for AI software integration, or edge / cloud / datacenter deployments.

Solution Architect - Telco Network

QA Automation Engineer

• Oversee and optimize compute and storage resources, ensuring operational efficiency and success of VLSI projects.
• Develop and maintain scripts, automation tools, and AI-assisted workflows to streamline infrastructure tasks and improve developer productivity.
• Design, implement, and maintain dashboards for monitoring and reporting on infrastructure performance and usage.
• Work closely with a remote team in Israel, ensuring smooth communication and coordination on infrastructure-related tasks.
• Engage in the entire lifecycle of infrastructure tool development, including design, testing, deployment, and maintenance.
• Collaborate with other team members and DA teams to implement innovative software solutions and agentic coding practices using modern AI tools.

VLSI Design Automation Software Engineer

AI Engineer

• Contribute features, fixes, and optimizations upstream to vLLM/SGLang
• Implement and optimize inference‑runtime capabilities
• Profile and improve hot paths across layers
• Improve multi‑GPU inference performance and reliability
• Build and maintain performance and correctness regression tests
• Collaborate with model, platform, and SRE teams

Senior Software Engineer – AI Inference

• Verify the design and implementation of the industry's leading GPUs
• Responsible for verification of the ASIC design, architecture, golden models and micro-architecture using advanced verification methodologies such as UVM
• Understand the design and implementation of your unit, define the verification scope, develop the verification infrastructure and verify the correctness of the design
• Collaborate with architects, designers, and pre and post silicon verification teams to accomplish your tasks

ASIC Verification Engineer – GPU

• Design and build performance analysis tools and workflows for AI training and inference workloads.
• Understand how AI performance engineers work and translate their needs into scalable, intuitive tooling.
• Develop integrations between profiling infrastructure and AI frameworks and workflows.
• Collaborate with performance engineers, hardware architects, and software teams to ensure profiling capabilities align with real-world AI workloads.
• Identify performance bottlenecks in AI workloads and develop automated approaches to detect and diagnose them.

Senior Software Engineer, AI Performance Analysis

• Use AI for solving product problems in gaming and other interactive experiences.
• Build upon the latest research to create new models and invent new ways of applying AI to advance neural graphics and gaming.
• Create prototypes to demonstrate real-life applications of your ideas and to accelerate productization.
• Keep up with the latest AI/DL research and collaborate with diverse teams (both internal and external to NVIDIA), including AI/DL researchers, hardware architects, and software engineers.
• Participate in technology transfers to and from teams across NVIDIA.

Senior Machine Learning Graphics Engineer, AI for Experiences

Engineering Manager

• Lead and grow a multi-team engineering organization, setting a high bar for software quality, service reliability, and engineering culture.
• Own roadmap execution for NVIDIA's internal object storage service — partnering with internal customers, Product Management, and Architecture to translate multi-quarter goals into clear engineering plans with measurable milestones.
• Drive development and operation of NVIDIA's S3-compatible object storage service, ensuring it meets the performance, durability, availability, and scalability demands of AI training and inference workloads at exabyte scale.
• Lead the Data Movement Tools team in building and evolving tooling that stages datasets, model checkpoints, and artifacts from distributed storage to GPU-adjacent compute — minimizing I/O bottlenecks and keeping accelerators fully utilized.
• Define and uphold service reliability standards: SLOs, capacity planning, incident response, root cause analysis, and on-call hygiene.
• Partner with SRE to ensure the platform meets the availability commitments internal customers depend on.
• Establish and enforce engineering standards across both teams: design reviews, code quality, CI/CD practices, automated testing, and production observability.
• Recruit, mentor, and develop engineers across all levels, conducting regular 1:1s, performance cycles, and career growth conversations.
• Build a diverse, inclusive, and high-retention team.
• Collaborate closely with SRE, Platform, Networking, and Security teams to ensure smooth transitions from development to production and rapid resolution of customer-impacting issues.
• Champion the adoption of AI-assisted development tooling — coding assistants, agentic workflows, and automated testing harnesses — to accelerate team productivity and raise engineering output.
• Represent the Object Storage engineering organization to senior leadership, providing transparent status updates, surfacing risks early, and advocating for the resources needed to succeed.

Senior Engineering Manager, Object Storage – DGX Cloud

Systems Engineer

• Design, build, and deploy highly scalable cloud-based solutions
• Architect and manage Kubernetes clusters with custom components
• Build advanced CI/CD pipelines and workflows
• Design observability solutions and tooling
• Mentor team members and drive best practices

Senior System Software Engineer – Cloud, GeForce NOW

• Collaborating closely with customers to improve their workload performance and reduce infrastructure costs.
• Leading and developing proof-of-concepts for AI solutions applied to the Consumer Internet industry, including areas like LLMs and recommenders, and building collateral (notebook/code) as needed.
• Developing and debugging software for NVIDIA and open-source AI frameworks and libraries.
• Partnering with NVIDIA’s software engineering, product, and sales teams to secure design wins and drive the development of innovative solutions based on customer feedback.