LLM Engineer

• Lead bring-up, validation, and debugging of large-scale AI clusters, infrastructure, and end-to-end workloads, setting the standard for how the team operates.
• Bring up, tune, and benchmark AI pre-training, post-training, and inference workloads using PyTorch, NeMo / Megatron, TensorRT-LLM, and adjacent NVIDIA AI software stacks.
• Profile and optimize end-to-end workload performance across compute, memory, networking, and communication layers using tools such as Nsight Systems, NCCL tests, and custom microbenchmarks.
• Analyze scaling efficiency for distributed LLM workloads using data, tensor, pipeline, and expert parallelism across modern GPU clusters, and translate findings into concrete tuning guidance.
• Own root-cause analysis of complex failures — hangs, performance regressions, topology sensitivity in large distributed environments.
• Define and build the resilience and failure-attribution stack: detecting, triaging, and attributing node, fabric, and workload failures across the cluster at scale.
• Build repeatable benchmark suites, automation, acceptance criteria, and qualification workflows on new platforms.
• Tune runtime settings, communication parameters, and deployment configurations in close partnership with framework, systems, and platform teams.
• Deliver actionable, data-driven recommendations based on profiling, benchmark results, and cluster characterization.
• Mentor engineers, drive technical standards, and act as a force multiplier across the broader performance and infrastructure organization.

Senior Software Engineer, DGX Cloud AI Infrastructure

• Bring up, validate, and debug large-scale AI clusters, infrastructure, and end-to-end workloads.
• Bring up, tune, and benchmark AI pre-training, post-training, and inference workloads using PyTorch, NeMo / Megatron, TensorRT-LLM, and adjacent NVIDIA AI software stacks.
• Perform root-cause analysis of failures in large distributed environments.
• Contribute to the resilience and failure-attribution tooling that detects, triages, and attributes node, fabric, and workload failures across the cluster.
• Build and maintain repeatable benchmark suites, automation, acceptance criteria, and qualification workflows on new platforms.
• Tune runtime settings, communication parameters, and deployment configurations in close partnership with framework, systems, and platform teams.
• Deliver actionable, data-driven recommendations based on profiling, benchmark results, and cluster characterization.

Software Engineer, DGX Cloud AI Infrastructure

• Ship production GenAI features that improve customer workflows
• Build reliable patterns for LLM integration, tool use, evaluation, and observability
• Improve the accuracy, latency, cost, and trustworthiness of AI-enabled systems
• Help teams move from prototypes to maintainable production services
• Partner across engineering, product, data, security, and business teams
• Contribute to responsible AI practices in an enterprise software environment

Generative AI Engineer

• Define and evolve the long-term AI/ML research strategy and technical roadmap for Trase OS in alignment with product and platform direction.
• Lead large-scale experimentation and prototyping efforts requiring significant compute infrastructure, translating frontier AI research into scalable, production-grade systems with measurable impact.
• Drive original research and technical breakthroughs in agentic systems, autonomous execution, multi-agent orchestration, post-training and fine-tuning systems, SLM/LLM-based architectures, and applied AI infrastructure.
• Design how models operate within long-lived execution environments, including agent workflows, tool use, planning, memory systems, reasoning, and human-in-the-loop controls.
• Establish evaluation methodologies and reliability frameworks for autonomous systems, including benchmarking, regression testing, safety, controllability, and production behavior analysis.
• Drive architecture decisions across orchestration, model serving, routing, inference, and infrastructure governance, including latency, reliability, and cost optimization.
• Partner closely with engineering and product teams to operationalize research outcomes into deployable systems and enterprise workflows.
• Build AI systems that operate reliably in regulated and constrained environments, including secure cloud, on-premise, and air-gapped deployments.
• Contribute to the broader AI research community through technical papers, publications, conference participation, architecture proposals, and thought leadership.
• Serve as a senior technical authority and mentor across the organization, influencing technical direction, research rigor, experimentation practices, and best practices across research, engineering, and product teams.

Principal AI Researcher – Agentic Systems, AI Infrastructure

• Identify, vet, and manage Tier 1/2 OEMs and regional distributors for high-density servers, network gear, and cabling.
• Drive end-to-end contract lifecycles, including Master Purchase Agreements (MPAs), Service Level Agreements (SLAs), and complex warranty/support negotiations.
• Monitor global semiconductor trends to mitigate long-lead-time risks. Support Solution Engineering by ensuring "just-in-time" inventory of mission-critical hardware (GPUs, NICs, Switches).
• Partner with Systems Engineering and Architecture teams to translate technical specs into scalable, multi-year procurement roadmaps.
• Oversee the procurement and delivery of integrated components, including NVIDIA Grace CPUs, NVLink, InfiniBand, and ConnectX-8 technologies.
• Architect procurement workflows that satisfy stringent security, data residency, and national compliance requirements for Sovereign AI cloud deployments.

AI Infrastructure Supply Chain Lead

• Develop and implement the AI Market and Solutions strategy to enable high-quality, trusted, and evidence-based AI solutions in education.
• Oversee a portfolio of investments and partnerships that catalyze responsible innovation in AI and EdTech.
• Identify and manage key portfolio risks, developing and implementing mitigation plans.
• Partner with the Strategy, Planning, and Management (SPM) team to develop annual investment plans and budgets.
• Guide the generation and use of evidence to assess AI tools’ efficacy, safety, and fairness.
• Strengthen market incentives and infrastructure for transparent and validated AI adoption across learning and workforce systems.
• Build and lead coalitions with AI companies, EdTech providers, research institutions, and funders to advance responsible AI practices.
• Apply systems thinking to connect data innovations with educational and workforce outcomes.
• Manage partnerships and grantee relationships through site visits, convenings, and evaluation of progress toward milestones.
• Model equitable decision-making and inclusive leadership behaviors, addressing issues promptly and constructively.
• Ensure the team has the resources and conditions needed to thrive and deliver impact.

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Senior Software Engineer, DGX Cloud AI Infrastructure

Software Engineer, DGX Cloud AI Infrastructure

Generative AI Engineer

Principal AI Researcher – Agentic Systems, AI Infrastructure

Principal AI Researcher – Agentic Systems, AI Infrastructure

AI Infrastructure Supply Chain Lead

Deputy Director, AI Infrastructure – Education