> Markdown version of [/jobs/ext/3843245-director-forward-deployed-engineering-open-models](https://www.wearedevelopers.com/jobs/ext/3843245-director-forward-deployed-engineering-open-models). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Director, Forward Deployed Engineering - Open Models - **Company:** NVIDIA Corporation - **Location:** Santa Clara, CA, United States - **Experience:** Expert - **Salary:** $320,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Computer Clusters, Distributed Computing Environment, Machine Learning, Performance Tuning, Software Engineering, Reinforcement Learning, Transfer Learning, Large Language Models, Kubernetes, Information Technology, Slurm, Megatron, Serverless Computing - **Published:** October 20, 2026 - **Apply:** https://nvidia.wd5.myworkdayjobs.com/NVIDIAExternalCareerSite/job/US-CA-Santa-Clara/Director--Forward-Deployed-Engineering---Open-Models_JR2028004 ## About the Role * Bachelor's, Master's, or PhD in Computer Science, Engineering, Machine Learning, or a related field, or equivalent experience. * 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. * A track record of building and scaling technical organizations across multiple locations and time zones, including managing managers or leads, and setting goals and measuring results for a customer-facing technical team. * Deep technical credibility in post-training (LoRA/PEFT, SFT, and synchronous and asynchronous reinforcement learning), model architectures, synthetic data, and evaluation, plus agent harness engineering and optimization for LLM and agentic systems. Good understanding of large-scale distributed training and inference performance optimization across multi-GPU and multi-node systems. * Deep understanding of the partner, open model, and cloud provider ecosystem, and where NVIDIA can add the most value. 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. * Proven ability to scope engagements, shape SOWs, and deliver consistently high quality across many engagements at once. * Clear communication of complex technical ideas, from engineers to C-level executives. Excellent judgment, prioritization, and organizational skills, and comfort leading through ambiguity. Travel to partner and customer sites and NVIDIA offices up to [25%]. Ways to Stand Out from the Crowd: * Experience building and scaling an FDE, applied research, or solutions organization from an early stage. * History of building partner-led programs, ecosystems motions. * Hands-on experience with Nemotron, Cosmos, and other open models, as well as serverless and self-managed post-training platforms (such as the Tinker API) and open-source frameworks such as NVIDIA NeMo and Megatron. * A technical vision that influences product, engineering, and business decisions and drives production outcomes. * Experience with multimodal or world-foundation-model post-training, and running training jobs on large GPU clusters with SLURM or Kubernetes with a passion for using AI tools to improve how the team builds, delivers, and scales. ## Description * This role sets the strategy and leads the team that delivers custom AI to end customers through NVIDIA's partner ecosystem. The team deepens partner relationships and leads three-way engagements among NVIDIA, the partner, and the end customer. Our goal is hundreds of end customers running domain-specific Nemotron or Cosmos in production, supported by a strong ecosystem that delivers it. * 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. * The work stays technical. 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. Keep up with the open model, post-training, and agents landscape, and bring new use cases and partner opportunities forward before partners ask. * Partners are how we reach customers at scale. Build trusted relationships with leaders at partner and end-customer companies, shape joint plans and agreements, and see three-way engagements through to production. Set clear goals and track the results: partners adopting Nemotron, customers running in production, and customers willing to speak publicly. 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. 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. 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!