- Location
- Santa Clara, CA,US, US
- Type
- Full-time
- Department
- IT
- Seniority
- Lead
- Source
- Eightfold
Description
Serve as a technical advisor and problem solver with partner engineering teams, collaborating on architecture, code, and integration for Omniverse and AI enabled-solutions. Develop and maintain deep technical expertise in NVIDIA Cosmos and Omniverse Platforms and related technologies (APIs, USD, NIMs, Blueprints) through prototyping, technical integration and creation of reference architectures. Advise on technical enablement resources such as sample code, guides, demonstration pipelines, and tools to highlight the application of technologies in solving real-world problems. Engage with partner software organizations, from engineering teams to technical leaders, and decision-makers to understand their goals, solve technical challenges, and promote best practices for successful integrations. Represent and advocate for the partner technical needs and feedback to NVIDIA's internal product and engineering teams, supplying actionable insights from field deployments to influence product roadmaps. Support product launches, technical go-to-market activities by providing technical validation, demonstrating integrated solutions, and ensuring excellence in customer- and partner-facing materials. Guide partners and startups through onboarding and integration with NVIDIA's programs, fostering co-innovation and the development of next-generation solutions. Experience writing production code in Python or C++, and proficiency with Linux. Experience with DL and RL algorithms and frameworks such as PyTorch. Experience leading technical collaborations with engineering and product teams—including architectural design, code reviews, technical mentorship, and delivery of technical talks or workshops. Experience with AV sensors, data curation pipelines, world models, simulations workflows and tools. Experience with Agentic AI frameworks, tools, and protocols like LangChain, LangGraph, MCP or equivalent experience. Experience in deploying LLM models at scale on mainstream cloud providers (e.g., AWS, Azure, GCP) with a proven track record to profile and optimize inference latency and throughput, memory and I/O utilization.
