NVIDIA

Product Manager

• Drive product strategy and roadmaps for portfolio projects
• Set priorities, quality bars, and product outcomes with research and engineering teams
• Engage agent builders, observability and evaluation tool providers, and partners to secure early-access engagements
• Make joint research and integrations successful and turn market feedback into research directions and stronger product capabilities
• Translate research findings and techniques into usable products and features
• Define developer experience and product interfaces for open source tools
• Improve reliability, usability, documentation, benchmark coverage, and interoperability across tools
• Build adoption motion with Product Marketing, Solution Architects, and GTM teams
• Identify qualified partners and workloads, guide pilots, and create repeatable adoption playbooks
• Establish messaging and narrative for agent optimization and continuous improvement loops
• Define and inspect evidence of success across research, products, partners, open source adoption, and publications
• Own outcomes and decide what to scale, change, stop, or incubate next

Senior Product Manager, Agent Optimization

• Drive Switchyard’s product strategy and roadmap, setting priorities, quality bars, and product outcomes with Engineering
• Work with agent harness builders, inference gateways, model serving systems, and partners to make integrations successful
• Define the developer experience and product interfaces for adaptive inference
• Partner with Research to translate cost and quality prediction, constrained optimization, and budget allocation advances into products and features
• Partner with Engineering to improve reliability, usability, documentation, SDK coverage, and production customization
• Build adoption motions with Product Marketing, Solution Architects, and GTM teams
• Identify qualified workloads, guide partner pilots, and build repeatable deployment playbooks
• Establish messaging and narrative for model routing and adaptive inference with Product Marketing
• Define and inspect evidence for success and own outcomes
• Coordinate across adjacent teams to build joint product outcomes

Senior Product Manager, Switchyard

Full-stack Engineer

• Develop and optimize Rust-based data processing frameworks for large datasets on GPU-accelerated environments supporting LLM training
• Lead development and iterative optimization of Agentic Retrieval pipeline components, including GPU acceleration and model performance optimization
• Collaborate with LLM and ML researchers on full-stack, GPU-accelerated data preparation pipelines for multimodal models
• Benchmark, profile, and optimize algorithms in Python across system architectures for LLM applications
• Understand requirements, build and evaluate proofs of concept, and develop roadmaps for production tools and library features
• Build products improving employee productivity through generative AI and copilot experiences
• Develop integrated systems enabling unified experiences across applications and end-to-end user insights
• Help build and maintain Continuous Delivery pipelines to move changes to production faster and safer
• Provide peer reviews covering performance, scalability, and correctness; contribute to frameworks, standards, and technology adoption

Senior System Software Engineer, Agentic Retrieval

Production Engineer

• Build and operate production software, automation, and tooling for control plane services, model deployments, and inference and agentic workloads across DGX Cloud environments
• Improve the reliability of inference and agentic platforms and services, including NVIDIA Cloud Functions, SGLang- and vLLM-based endpoints, and inference services built with NVIDIA Dynamo
• Improve endpoint availability, inference routing, capacity management, and service health
• Use infrastructure as code and GitOps to deploy, configure, validate, upgrade, and recover services consistently across environments
• Build workflows for service enablement, model releases, handoff, deprecation, and ongoing operations
• Define and instrument SLIs and SLOs for inference and control plane services and use error budgets to guide reliability improvements
• Participate in on-call and incident response, troubleshoot failures, and turn recurring issues into automation and durable fixes
• Collaborate with model, platform, storage, networking, security, and GPU infrastructure teams to design and operate services safely at scale

Senior Production Engineer – DGX Cloud

• Work closely with architecture, hardware, and driver teams through the product development lifecycle of computing and graphics processors and compute products
• Craft software tools and infrastructure required for new chip development, validation, and productization
• Assess new hardware features and architect diagnostic software using pre-beta CUDA extensions
• Develop robust and efficient solutions to validate and test next-generation processors

Senior System Software Engineer, GPU Server

Artificial Intelligence

• Deploy, manage, and validate AI Compute/HPC infrastructure in Linux-based environments for new and existing customers
• Act as the domain expert with customers from planning calls through implementation
• Interact with customers, partners, and internal teams to analyze, define, and implement large-scale AI Compute projects
• Work across networking, system design, automation, and validation
• Prepare handover-related documentation and perform knowledge transfers as customers begin rolling out sophisticated systems
• Provide feedback to internal teams by opening bugs, documenting workarounds, and suggesting improvements

AI Compute Engineer

Engineer

• Own the technical vision, architecture, and roadmap for 3D obstacle perception supporting end-to-end autonomous driving
• Design and develop 3D perception models using multi-camera inputs and/or camera, radar, and lidar sensor fusion
• Lead development of efficient, production-grade deep learning models
• Define objectives, select architectures, guide experimentation, and establish training and evaluation best practices
• Define and drive KPI frameworks for perception performance
• Analyze large-scale real and synthetic datasets to identify failure modes and improve accuracy, robustness, and efficiency
• Lead perception data strategy, including data and labeling requirements, collection, annotation, active learning, auto-labeling, VLMs, and model-in-the-loop tooling
• Partner with safety, systems, and software teams to meet product requirements for safety, latency, resource usage, and software robustness
• Drive deployment readiness at scale
• Provide technical leadership and mentorship across perception and autonomy teams

Principal Perception Engineer – Obstacle Foundation Models, Autonomous Vehicles

Compliance

• Lead the lifecycle, planning, execution, and improvement of security certifications including ISO 27001, SOC 2, and NIST 800 series
• Define, implement, and operate compliance and audit processes while improving certification workflows
• Collaborate with internal teams to reduce certification costs and complexity
• Drive certification request triage and prioritization
• Manage audit onboarding, preparation, and external assessment
• Own delivery of the certification roadmap, including reporting and escalations
• Coordinate export control-related continuous compliance audits
• Recommend and implement process improvements for efficiency and compliance with evolving standards

Governance, Risk, and Compliance Engineer

• Develop and improve the technical design, architecture, and roadmap for 3D obstacle perception supporting end-to-end autonomous driving
• Design and implement advanced 3D perception models using multi-camera inputs and/or multi-sensor fusion for obstacle detection and tracking
• Build efficient, production-grade deep learning models by defining objectives, selecting and prototyping architectures, running experiments, and applying training and evaluation best practices
• Define and maintain KPI frameworks to quantify perception performance
• Analyze large-scale real and synthetic datasets to identify failure modes and improve accuracy, robustness, and efficiency
• Contribute to perception data strategy, including data and labeling requirements, collection and annotation priorities, and model-assisted workflows
• Collaborate with data and ground-truth teams on active learning, auto-labeling, vision-language models, and model-in-the-loop tooling
• Collaborate with safety, systems, and software teams to meet product requirements for safety, latency, resource usage, and software robustness
• Prepare perception solutions for deployment at scale

Senior Perception Engineer – Obstacle Foundation Models, Autonomous Vehicles

• Verify the design and implementation of industry-leading GPUs
• Verify ASIC design, architecture, golden models, and micro-architecture using advanced methodologies such as UVM or equivalent
• Understand assigned unit design and implementation, define verification scope, develop verification infrastructure, and verify design correctness
• Collaborate with architects, designers, and pre-/post-silicon verification teams

Senior ASIC Verification Engineer

• Develop and improve the technical design, architecture, and roadmap for radar-based 3D obstacle perception supporting end-to-end autonomous driving
• Conduct applied research on deep learning models to maximize information from radar point cloud data
• Address radar perception challenges including low and non-uniform angular resolution, multipath and ghost targets, micro-Doppler signatures, and class imbalance
• Explore weakly-supervised pretraining and improve radar perception using large auto-labeled datasets
• Design and implement 3D perception models using radar inputs and camera, radar, and lidar fusion
• Develop obstacle detection, tracking, and Bird’s-Eye-View scene understanding systems
• Drive radar sensor evaluation, selection, and layout optimization for L2-L4 autonomous driving applications
• Build efficient, production-grade deep learning models from objective definition and architecture prototyping through experimentation, training, and evaluation
• Apply large-scale radar pretraining, cross-modal distillation, and parameter-efficient fine-tuning
• Define and maintain KPI frameworks for radar perception performance
• Analyze real and synthetic datasets to identify radar-specific failure modes and improve accuracy, robustness, and efficiency
• Contribute to radar data strategy, data collection and annotation prioritization, and model-assisted auto-labeling workflows
• Collaborate with data and ground-truth teams
• Collaborate with safety, systems, and software teams to ensure product requirements for safety, latency, resource usage, robustness, and scalable deployment are met

Senior Radar Perception Engineer, Obstacle Foundation Models – Autonomous Vehicles

Application Engineer

• Serve as a technical specialist on GPU and networking products, supporting manufacturing, quality, and failure analysis teams to improve product reliability and yield
• Establish and nurture technical relationships with internal engineering teams, manufacturing partners, and quality teams
• Identify system failure modes, manufacturing risks, and product requirements to drive corrective actions and design/process improvements
• Maintain frequent on-site presence in labs or manufacturing sites to diagnose, reproduce, and resolve hardware and system-level failures
• Support the product lifecycle from NPI through high-volume manufacturing and field returns
• Develop failure analysis methodologies, test plans, and diagnostic tools for hardware validation
• Provide technical training and documentation to manufacturing, test, and quality teams on failure modes and debugging techniques
• Collaborate with design, validation, manufacturing, and quality teams to drive issue resolution, root cause determination, and corrective actions

Senior Field Application Engineer

Solutions Engineer

• Lead strategic agentic AI partner engagements from discovery and architecture through PoC, production readiness, rollout, and scale
• Build enterprise-grade agentic AI systems with multi-agent workflows, tool-using agents, RAG, planning, memory, evaluation, guardrails, policy enforcement, and failure containment
• Partner with security ISVs to integrate NVIDIA models into detection and response products
• Architect secure and confidential AI deployments using NVIDIA Confidential Computing, GPU attestation, KMS integration, protected infrastructure, air-gapped patterns, and partner key-management workflows
• Create PoCs, benchmarks, reference architectures, reusable blueprints, field guidance, and product feedback to move secure AI systems into production
• Improve NVIDIA’s factory planning function

Senior Solutions Architect – Agentic AI, Safety and Security

AI Engineer

• Develop GPU-accelerated techniques for generative AI, deep learning, and machine learning workloads
• Optimize generative AI training and inference on NVIDIA platforms with key developers
• Build and optimize algorithms and contribute to training and inference frameworks, low-level software libraries, and open-source projects
• Analyze and optimize complex AI algorithms on modern CPU and GPU architectures with industry and academic experts
• Collaborate with NVIDIA engineering and research teams on next-generation hardware, system software, libraries, and programming models
• Publish optimization techniques in developer blogs and present at industry conferences

AI Developer, Technology Engineer

• Research, design, and implement novel embodied and agentic AI methods
• Collaborate with a mentor and broader research team to develop ideas from hypothesis through rigorous experimentation
• Partner with product and engineering groups to transfer research breakthroughs into real-world applications
• Rapidly prototype and iterate on ideas using state-of-the-art hardware and infrastructure
• Publish internship project results at top computer vision and machine learning venues