NVIDIA

Machine Learning Engineer

• Performance optimization, analysis, and tuning of DL models in various domains like LLM, Multimodal and Generative AI.
• Scale performance of DL models across different architectures and types of NVIDIA accelerators.
• Contribute features and code to NVIDIA’s inference libraries, vLLM and SGLang, FlashInfer and LLM software solutions.
• Work with cross-collaborative teams across frameworks, NVIDIA libraries and inference optimization innovative solutions.

Deep Learning Software Engineer, Inference

• Explore and incorporate contemporary research on generative AI, agents, and inference systems into the NVIDIA LLM software stack.
• Conduct in-depth analysis, profiling, and optimization of agentic LLM workloads to significantly reduce request latency and increase request throughput while maintaining workflow fidelity.
• Design and implement scalable systems to accelerate agentic workflows and efficiently handle sophisticated datacenter-scale use cases.
• Advise future iterations of NVIDIA software, hardware, and system by engaging with a diverse set of teams at NVIDIA and external partners and formalizing the strategic requirements presented by their workloads.

Principal Deep Learning Algorithm Engineer

Developer Relations

• Establish trusted partner relationships with wireless research community in the public sector, e.g. US and other Government and public sector entities.
• Lead and implement NVIDIA’s strategic goals in 6G and AI Win projects to address public sector use cases.
• Engage with public sector partners and drive the ecosystem to bring groundbreaking innovation on NVIDIA’s platform.
• Collaborate with cross-functional teams, including engineering, product management, and NVIDIA’s Govt. relations teams, to drive tasks aligned with our mission.
• Lead and contribute to industry organizations such as ATIS, AI-RAN and 3GPP.
• Analyze sophisticated challenges in realization of AI-native RAN, develop strategies, and implement solutions to achieve project objectives.
• Maintain up-to-date knowledge of industry trends and emerging technology to drive innovation.
• Ensure project deliverables meet or exceed quality benchmarks and align with NVIDIA’s standards of perfection.

Senior Developer Relations Manager – Government & Defense

Technical Program Manager

• Collaborate with hardware, software, and model program managers, product managers and engineering teams to deliver Deep Learning Frameworks programs on existing and new hardware.
• Engage with cross-company teams (hardware/software engineering, product, QA, compliance) and drive alignment on release scope, milestones, risk management, and dependencies.
• Guide software programs in all aspects of program management – planning, forecasting, documenting, scheduling, effective meetings, multi-faceted prioritization, management of dependencies, reporting, and effective handling of critical and blocking issues.
• Develop and implement metrics for measuring program effectiveness and improvement areas, collect and analyze data in support of planning and data driven decisions.
• Define and implement standard processes for open-source contribution and release management within the Nvidia AI/ML ecosystem.
• Report on overall program status, providing insights and recommendations to senior management.
• Drive organizational alignment and efficiency by coordinating with multi-functional leads and streamlining processes.

Senior Technical Program Manager, Deep Learning Frameworks

Full-stack Engineer

• develop innovative solutions that advance AI infrastructure capabilities.
• directly influence customer success with breakthrough AI initiatives.
• build and deploy custom AI solutions on NCP and Neo Cloud platforms, including distributed training, inference optimization, and MLOps pipelines constructed on NVIDIA reference architectures.
• act as the main technical contact for strategic NCPs, offer remote and on-site support, troubleshoot complex production problems, and guide partner engineering teams on NVIDIA platform guidelines.
• deploy and manage AI workloads across DGX Cloud, NCP data centers, and major CSP environments using Kubernetes, containers, and GPU scheduling systems aligned to NCP builds.
• profile and tune large-scale training and inference workloads on NCP platforms.
• implement observability and SLO/SLA monitoring.
• lead detailed efforts to reduce latency, cost, and operational risk.
• implement and expand NVIDIA reference architectures on partner platforms, develop integrations with partner control planes and customer environments, and ensure smooth API, data pipeline, and enterprise software connectivity.
• build detailed implementation guides, runbooks, and post‑mortem documentation that codify standard methodologies for running NVIDIA AI workloads at scale on NCP platforms.

Senior Engineer

Solutions Engineer

• Providing Ethernet and routing expertise to customers during project delivery
• Work in multi-functional teams to provide Ethernet networking expertise
• Crafting and evaluating DevOps automation scripts for network operations
• Implementing tasks related to network configuration and validation for data centers
• Create Methods of Procedure and deployment documents
• Validate network architectures and configs in the lab
• Create network automation scripts
• Interact directly with customers to obtain required information
• Use software tools to validate and monitor network performance
• Create project documents to hand over to the customer
• Travel onsite to the customers' data Centers

Senior Solutions Architect, Ethernet Networking

Mechanical Engineer

• You will drive physical design of high-frequency and low-power CPUs, GPUs, SoCs at block level, cluster level, and/or full chip level, with a focus on netlist-related aspects such as equivalence checking, asynchronous checking including clock domain crossing checks and MTBF analysis, logic synthesis, netlist quality checks, etc.
• Help in all aspects of physical design, such as driving timing convergence, timing constraints generation and management, and ECO generation and implementation.

Senior ASIC Physical Design Engineer, Netlisting

Backend Engineer

• Designing, implementing, and delivering innovations for managing GPU based AI servers with focus on OOB management, firmware development, server architecture and building systems for enterprise
• Leading BMC firmware design with a global team of engineers
• Designing and developing performance optimized active monitoring BMC solutions using DMTF Standards including MCTP, Redfish, SPDM and PLDM specifications
• Instrumenting code to ensure maximum code coverage
• Writing and automating unit tests for each implemented module and maintain detailed unit test case reports
• Providing software quality reports based on static analysis, code coverage, CPU load
• Working with security team to ensure developed code is in line with product security goals
• Working closely with hardware teams to influence hardware design and review HW architecture & schematics
• Driving definition and end to end delivery of all platforms by collaborating with internal teams, ODMs/OEMs and industry partners for AI servers
• Working with QA/Test architects to come up with proper test tools and automation for qualifying the whole system software and firmware stack

Senior Software Engineer – Server Manageability

Cloud Engineer

• Design and develop APIs to orchestrate and integrate operational workflows.
• Build state management and workflow automation systems that streamline infrastructure lifecycle processes.
• Collaborate across teams to codify business processes into scalable, self-measuring systems.
• Develop extensible, schema-driven platforms for reducing manual toil and ensuring operational consistency.
• Drive integrations with container orchestration tools like Kubernetes and observability systems such as Prometheus, OpenTelemetry, Grafana.
• Optimize the reliability and efficiency of cloud operations through automated workflows and telemetry systems.
• Lead and ship impactful technical projects, ensuring quality and scalability at every stage.

Senior Software Engineer, DGX Cloud Orchestration

• Create new technical designs and implement features while working in an agile environment.
• Improve existing tools for NVIDIA GPUs, enabling developers to quickly identify and resolve rendering and performance issues.
• Create innovative new tools that bridge AI & graphics in next generation workflows.
• Collaborate closely with internal and external partners, including teams within NVIDIA such as the Compiler, Driver, Research, and GPU Architecture teams.
• Drive technology discussions and provide feedback on system architecture.
• Accurately estimate and prioritize tasks in order to create realistic delivery schedules.
• Write fast, effective, maintainable, reliable and well-documented object-oriented code.
• Document requirements and designs, and review documents with key partners.
• Meet with the QA Department to develop test plans for new features.

Software Engineer, Neural Graphics Developer Tools

LLM Engineer

• Collaborate closely with our AI and ML research teams to understand their infrastructure needs and obstacles
• Monitor and optimize the performance of our infrastructure ensuring high availability, scalability, and efficient resource utilization
• Help define and improve important measures of AI researcher efficiency, ensuring that our actions are in line with measurable results
• Collaborate with diverse teams, including researchers, data engineers, and DevOps professionals, to build a seamless and coordinated AI/ML infrastructure ecosystem
• Stay on top of the latest advancements in AI/ML technologies, frameworks, and effective strategies, and promote their implementation within the company

AI and ML Infra Software Engineer, GPU Clusters

AI Engineer

• Leading cloud VM platform initiatives from architecture and systems design through implementation, testing, and deployment.
• Designing, building, and operating scalable services that support secure, isolated agent workloads.
• Developing automation for infrastructure provisioning, VM lifecycle management, application delivery, configuration, monitoring, and remediation.
• Collaborating with engineering teams across NVIDIA to translate business and AI workflow requirements into dependable platform capabilities.
• Improving the platform’s reliability, scalability, observability, performance, and operational efficiency.
• Continuously raising standards for code quality, testing, documentation, infrastructure security, and production readiness.

Senior Software Engineer, AI Agent Compute

• Design and implementation of high-speed interface circuits
• Create projects including high speed transceivers and high frequency PLLs
• Design, simulation, and verification of mixed-signal circuits
• Lead mask designers and provide mentorship for floorplan and layout design
• Support lab characterization of silicon and circuit design in deep submicron CMOS
• Take designs through implementation and productizing
• Collaborate with multi-functional teams

Senior Mixed Signal Circuit Design Engineer

Architect

• Define the NVL system architecture end-to-end, by internal requirements and customers requirements through all product life cycles (post/pre silicon, on deployments)
• Research various of solutions to enable the next large-scale-high-performance computing clusters
• The position spans over various layers from algorithms, software, firmware, and HW
• Collaborate with cross-functional teams, including other architecture teams, logic design, system software, firmware, and research teams, to ensure the successful execution of the project

Senior High-Performance System Architect

• Build AI agents that solve real engineering problems across the networking stack — and ship them to daily use.
• Design and build the platforms that make those agents reliable: typed tool surfaces, cloud services, and the systems behind them.
• Work broadly across networking architecture teams to identify high-impact AI use cases and turn them into working solutions.
• Drive rapid and responsible AI adoption across the organization: establish best practices, guardrails, and evaluation methods so teams embrace agentic workflows with confidence.
• Build evaluation pipelines that measure and improve AI-agent behavior on real production workloads.