• Orchestrate Distributed Inference: Deploy and configure LLM-D and vLLM on Kubernetes clusters. • Optimize for Production: Go beyond standard deployments by running performance benchmarks, tuning vLLM parameters, and configuring intelligent inference routing policies to meet SLOs for latency and throughput. • Code Side-by-Side: Work directly with customer engineers to write production-quality code (Python/Go/YAML) that integrates our inference engine into their existing Kubernetes ecosystem. • Solve the "Unsolvable": Debug complex interaction effects between specific model architectures (e.g., MoE, large context windows), hardware accelerators (NVIDIA GPUs, AMD GPUs, TPUs), and Kubernetes networking (Envoy/ISTIO). • Feedback Loop: Act as the "Customer Zero" for our core engineering teams. You will channel field learnings back to product development, influencing the roadmap for LLM-D and vLLM features. • Travel only as needed to customers to present, demo, or help execute proof-of-concepts.

• Performing detailed task analysis, defining key quality drivers and necessary skills, developing efficient testing and onboarding methodologies for our Data Services vendors • Managing end-to-end production Control processes and providing feedback to suppliers • Reporting on delivered productivity and propose action plans for improvement • Addressing client quality escalations and collaborating with suppliers for root cause analysis • Implementing quality improvement plans for underperforming suppliers • Supporting data suppliers and providing timely answers to their queries

• Propose, research, prototype and test innovative research ideas. • Publish groundbreaking work at top conferences and journals. • Collaborate with other research team members, fellow interns, internal product teams, external researchers and be mentored. • Contribute to technology transfer with engineers around NVIDIA as ideas graduate from research to product. • Make good use of top-of-the-line NVIDIA GPUs at scale for cutting edge research at the intersection of AI and climate science.

• Run client QBRs on AI visibility metrics and competitive positioning • Develop custom GEO playbooks based on industry and competition analysis • Present strategic recommendations to C-suite stakeholders • Configure accounts and test prompt strategies for maximum AI citations and mentions • Collaborate with product team to shape platform features What You'll Own • Lead enterprise accounts through AI search transformation • Deliver monthly AI visibility reports with clear performance narratives • Increase client AI visibility by 40% within first 90 days • Generate 3+ case studies per month showcasing client wins • Translate complex AI search behavior into actionable business strategies • Drive platform adoption across client marketing and SEO teams