NVIDIA

Backend Engineer

• Analyze, design, and implement optimized GPU algorithms for large-scale vector search, databases, and machine learning.
• Expand and improve the integration of NVIDIA cuVS into relevant high-level vector search libraries and vector databases.
• Drive performance analysis, benchmarking, and troubleshooting of associated libraries.
• Collaborate with a multi-functional team to understand requirements and implement or improve solutions

Senior Java Acceleration Engineer, Vector Search, Database Acceleration

Full-stack Engineer

• Design and implement resource allocation and combinatorial optimization techniques to optimize LLM models at datacenter scale.
• Research, develop, and deploy AI/ML techniques to optimize large-scale Deep Learning training and inference on NVIDIA supercomputers and distributed systems.
• Build and productionize ML-based tools for performance prediction and optimization with a strong emphasis on networking.
• Develop and deploy a scalable, reliable data curation pipeline capable of handling complex data types.
• Collaborate across hardware and software teams to deliver valuable performance analysis insights.
• Lead performance test planning, establish performance targets for new technologies and solutions, and drive efforts to achieve those performance goals.

Software Engineer, AI Networking

Artificial Intelligence

• Become part of the team committed to progressing novel and inventive solutions in compilers and development tools, focusing on applied machine learning and artificial intelligence
• Join committed team members in leading tech sectors
• Innovate in machine learning, system design, and more
• Influence global products

Senior Compiler Engineer – AI

Research Engineer

• Work with applied researchers to design, implement and test next generation of RL and pos-training algorithms
• Contribute and advance open source by developing NeMo-RL, Megatron Core, and NeMo Framework and yet to be announced software
• Solve large-scale, end-to-end AI training and inference challenges, spanning the full model lifecycle from initial orchestration, data pre-processing, running of model training and tuning, to model deployment.
• Work at the intersection of computer-architecture, libraries, frameworks, AI applications and the entire software stack.
• Performance tuning and optimizations, model training with mixed precision recipes on next-gen NVIDIA GPU architectures.
• Publish and present your results at academic and industry conferences

Senior Research Engineer

Manager

• Lead Global Support Operations: Take full ownership of the NPN Portal support and onboarding functions, managing a high-volume environment that resolves over 13,000+ external support cases and hundreds of internal applications annually.
• Architect AI-Driven Support: Drive the strategy to build and deploy AI agents and automation to complement existing team members, ultimately establishing a 24/7 service model for our global users.
• Manage Regional Teams: Provide direct leadership and mentorship to our operations support team in Pune, India, ensuring alignment with HQ objectives.
• Optimize Partner Onboarding: Enhance the end-to-end onboarding journey for thousands of monthly new users, including future foundational training initiatives for developers within our ecosystem.
• Strategic Realignment: Partner with Senior Leadership to transition daily support management away from the Business System Analyst team, allowing them to focus on foundational NPN architecture.
• Collaborate with team members from different areas to develop business requirements. Build processes and deploy system improvements that improve ease of doing business with NVIDIA.

Manager, NPN Portal Onboarding and Support

• Gathering use cases + requirements, translating those into software roadmaps, and executing those roadmaps across internal NVIDIA teams and external partners.
• Reporting project status, risks, help needed, and roadmap pivots to internal and external executives via status reports and in-person meetings.
• Brokering technical discussions between highly technical subject matter experts
• Leveraging AI tools and workflows to quickly iterate on designs, prototypes, documentation, tests, and code.
• Architecting distributed, robust, and scalable GoLang and Rust system software, deployed to monitor and manage large datacenters

Principal Engineer, Data Center Power Software

Solutions Engineer

• Assist various network and AI cluster support teams in reproducing, resolving, and root causing sophisticated customer issues
• Work with NVIDIA R&D teams to rapidly develop bug fixes, workarounds, and solutions for customers using NVIDIA’s network technologies
• Work directly with customer engineers on live triage of critical and high-profile issues
• Become an authority in NVIDIA Spectrum-X networking used in AI clusters
• Develop support and analysis tools to help analyze and root cause field issues
• Daily use of ground breaking AI tools for software development, log and trace analysis, and source code debugging
• Spend a portion of time working directly within an R&D team developing core software
• Occasional work on weekends or holidays to support critical customers

Senior Solution Engineer, Networking

• Design, implement, and maintain full‑stack features across frontend, backend services, and data layers, meeting defined reliability, availability, and performance expectations.
• Own delivery of well‑scoped components or features, from design through implementation, testing, and production support.
• Contribute to system reliability, performance, and observability improvements by identifying bottlenecks, fixing issues, and following established best practices.
• Collaborate with product managers, designers, and AI research stakeholders to understand user needs and translate them into clear technical solutions.
• Participate in design reviews, code reviews, and on‑call rotations to help maintain a high‑quality, production‑ready system.
• Follow and contribute to team standards for code quality, testing, security, and CI/CD.
• Learn and apply modern practices in cloud infrastructure, deployment, and monitoring with guidance from senior engineers.

Senior Full-Stack Software Engineer

• Build complex agentic systems featuring multi-agent coordination, long-horizon reasoning, and advanced 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.
• Collaborate alongside Enterprise ISVs to integrate NVIDIA software into native platforms, accelerating the deployment of production workloads.
• Collaborate with diverse internal teams to improve NVIDIA software through feedback from real-world implementations.
• Empower partner engineering teams through technical workshops, deep-dive architecture reviews, and developer enablement.
• Scale global expertise by crafting reusable assets and documentation that help field teams deploy agentic AI at scale.

Solutions Architect, Agentic AI

AI Research Scientist

• Help trailblaze company efforts in applying AI within conventional compilation pipelines
• Design and implement AI-based technology addressing core problems of low-level GPU programming
• Build training pipelines for supervised fine-tuning and reinforcement learning (RL/RLHF-style or policy optimization variants)
• Define model inputs/outputs over compiler low level compiler representations
• Develop evaluation frameworks to measure code quality, runtime, compile-time overhead, and correctness
• Intelligent (domain task based) prompt engineering
• Collaborate with compiler engineers to integrate learned policies into production toolchains
• Prototype and iterate on model architectures, prompts, and fine-tuning strategies for scheduling and allocation tasks
• Create datasets from compiler traces, optimization passes, and target-specific performance signals
• Apply RL techniques to optimize for downstream objectives (performance, spill reduction, instruction-level parallelism, etc.) and run rigorous experiments, ablations, and benchmarking across workloads and hardware targets

AI Research Engineer – Applied Scientist, Compilers

• Bring positive positioning of NVIDIA technology for multilateral and nation sovereign AI interests and within multilateral forums
• Promote positive role of AI in benefiting society and economic growth, represent the voice of NVIDIA in forums and events
• Develop and implement strategies to engage policy makers, government delegations, and partnerships with the private sector
• Craft a program framework for engaging with major convenings such as UNGA, APEC, and similar events through the year
• Team on our HQ Global Public Sector (GPS) Industry Business Development (IBD) plan with regional teams, internal and external peers to deliver on strategy
• Celebrate our breakthroughs and wins!
• Create strategic partnerships and coalitions with leaders at international, political, trade, think tank, and multilateral organizations to build multi-stakeholder approaches that support priorities
• Understand needs of specific regions to champion global case studies, blueprints, partner enablement, and event support.
• Available as industry and global expert.

Senior Global AI Initiatives Manager

Engineer

• Verify the build and implementation of the industry's leading GPUs
• Verify the micro-architecture using formal verification tools
• Define the verification scope and ensure correctness
• Employ sophisticated formal techniques to acquire sufficiently bounded proofs
• Work with architects, designers, and pre- & post-silicon verification teams
• Deliver high-quality results on schedule

Senior Formal Verification Engineer

Technical Program Manager

• Define and build the future of NVIDIA's self-driving software
• Responsible for the product life cycle and safety aspect of NVIDIA's stack
• Tracking the execution across the entire program to ensure delivery timelines can be met
• Work closely with developers and safety engineers to deliver best technology solutions
• Collaborate closely with experts in safety engineering to gain comprehensive grasp of expectations
• Interact directly with engineers and engineering leads to meet safety goals on time
• Coordinate with teams to deliver roadmaps: communicate and resolve bottlenecks
• Develop deep technical understanding of area of ownership, identify areas of improvement
• Escalate technical, resource, and compliance risks with mitigation plans
• Engage with internal teams to root cause workflow/tooling bottlenecks

Senior Technical Program Manager – Safety and Product Life Cycle, Autonomous Vehicles

• Lead the design and implementation of a real‑time, GPU‑accelerated propagation engine
• Architect and implement a GPU ray‑tracing engine that operates at two time scales
• Produce volumetric radio maps — coverage, SINR, and best‑server maps at multiple resolutions
• Deliver per‑link multipath channel updates at the millisecond cadence that a production RAN stack requires
• Lay the foundations for real‑time ray tracing on GPU hardware — algorithms, data structures, and multi‑GPU scaling strategies

Senior GPU Propagation Engine Engineer

• Lead the implementation of performance practices in large-scale GPU infrastructure, delivering powerful tools, methodologies, and flows to validate and improve multiple datacenter products concurrently.
• Align next-generation AI workloads with next-generation datacenter builds for NVIDIA GPUs, CPUs, and networking hardware.
• Engage early with HW/FW/SW/platform internal and customer teams.
• Develop engineering solutions that provide continuous insights into the performance of AI workloads in evolving environments, generating swift insights into improvements and regressions.
• Decompose high-complexity performance or stability issues into minimal reproduction cases, working towards identifying the root cause.
• Participate in collaborations with various SW and FW teams (BMC/SBIOS/OS/drivers, etc.) to develop outstanding methods and tools.
• Analyze, debug, and resolve critical firmware and software issues to achieve the highest AI workload performance at scale.