Nvidia
Director, Forward Deployed Engineering - Open Models
US, CA, Santa Clara · Director
Sponsorship not specifiedDetected 3 days ago
Node.jsKubernetesMachine LearningLLMsResearchLeadershipCommunicationOrganizational Skills
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odds of building a lasting career here
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Cap-exempt (no lottery)0
Sponsors this role100
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70
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Green-card filing pattern in this occupation
Context, not a finding about this posting: of this employer's 843 green-card filings in this occupation, 97% were for a worker who already held the job.
Green-card follow-through: 95%
Of 584 labor certifications old enough to have been used, 29 expired without the employer filing the next step. Median time from filing to decision: 497 days.97% of their filings were for a worker who already held the job.Only certifications past the 180-day window are counted — recent ones cannot have expired yet.
Files H-1B transfers
620 transfer filings in the last year, covering 1241 workers. Median labor-condition decision: 7 days. An employer that already files transfers is one that can take over an existing H-1B.
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About the role
- Set the technical direction for post-training, evaluation, harness engineering, and inference optimization, and be the person the team turns to on the hardest engagements.
- Work with Product, Research, and Engineering to bring what we see in the field back into Nemotron, Cosmos, and our software stack, and share what works with FDE leaders in other regions.
- 14+ overall years in ML or software engineering, applied AI, FDE, or solutions architecture, including 8+ years leading technical teams and 5+ years of hands-on LLM post-training with models taken to production.
Responsibilities
- The team deepens partner relationships and leads three-way engagements among NVIDIA, the partner, and the end customer.
- Hire and develop FDE leads, managers, and senior engineers across North America, and set a high bar for technical depth and customer impact.
- Create clear paths for growth and a team structure that scales with the ecosystem.
- Keep up with the open model, post-training, and agents landscape, and bring new use cases and partner opportunities forward before partners ask.
- Build trusted relationships with leaders at partner and end-customer companies, shape joint plans and agreements, and see three-way engagements through to production.
- We help partners succeed on their own with reusable recipes, reference pipelines, and playbooks, so more developers can build domain-specific AI.
- We connect NVIDIA's model research to partner and customer use cases.
- Align priorities and resources with sales, partner, and solutions leaders, keep senior leadership informed on progress, and speak on NVIDIA domain-specific open models.
- Above all, we're excited to help organizations worldwide build, own, and run their own AI on open source models!
- Deep understanding of the partner, open model, and cloud provider ecosystem, and where NVIDIA can add the most value.
Benefits
- This leader sets the strategy for North America, builds the organization, and carries the vision to partners, customers, and NVIDIA leadership.
Company info
- This role sets the strategy and leads the team that delivers custom AI to end customers through NVIDIA's partner ecosystem.
- Our goal is hundreds of end customers running domain-specific Nemotron or Cosmos in production, supported by a strong ecosystem that delivers it.
- Partners are how we reach customers at scale.
- Set clear goals and track the results: partners adopting Nemotron, customers running in production, and customers willing to speak publicly.
- Proven ability to build alignment across multiple partners and customers on the same engagement, including at the executive level.
- Good understanding of how to drive ecosystem demand and match partners with customers.
- Ways to Stand Out from the Crowd: Experience building and scaling an FDE, applied research, or solutions organization from an early stage.
This listing is sourced directly from Nvidia's careers page and normalized into a canonical job model.