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NVIDIA

Designs the chips and software that power AI

Director Forward Deployed Engineering - Open Models

Engineering LeadershipConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • Bachelor’s, Master’s, or PhD in Computer Science, Engineering, Machine Learning, or a related field, or equivalent experience.
  • 14+ overall years in machine learning or software engineering, applied artificial intelligence, forward deployed engineering, or solutions architecture, including 8+ years leading technical teams and 5+ years of hands-on large language model 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 (low-rank adaptation/parameter-efficient fine-tuning, supervised fine-tuning, and synchronous and asynchronous reinforcement learning), model architectures, synthetic data, and evaluation, plus agent harness engineering and optimization for large language model 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 statements of work, 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.
  • Willingness to travel to partner and customer sites and NVIDIA offices up to 25%.

Responsibilities

  • Set the strategy and lead the team that delivers custom AI to end customers through NVIDIA’s partner ecosystem.
  • Deepen partner relationships and lead three-way engagements among NVIDIA, the partner, and the end customer, aiming to support hundreds of end customers running domain-specific Nemotron or Cosmos in production.
  • Hire and develop forward deployed engineering leads, managers, and senior engineers across North America, set a high bar for technical depth and customer impact, create clear growth paths, and build a team structure that scales with the ecosystem.
  • Set technical direction for post-training, evaluation, harness engineering, and inference optimization, and serve as the technical escalation point on the hardest engagements.
  • Monitor the open model, post-training, and agents landscape, and bring forward new use cases and partner opportunities.
  • Build trusted relationships with partner and end-customer company leaders, shape joint plans and agreements, and see three-way engagements through to production.
  • Set goals and track partner adoption of Nemotron, customers running in production, and customers willing to speak publicly.
  • Help partners succeed independently with reusable recipes, reference pipelines, and playbooks so more developers can build domain-specific artificial intelligence.
  • Work with Product, Research, and Engineering to bring field insights into Nemotron, Cosmos, and NVIDIA’s software stack, and share effective practices with forward deployed engineering 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.

Desired Qualifications

  • Experience building and scaling a forward deployed engineering, applied research, or solutions organization from an early stage.
  • History of building partner-led programs and ecosystem 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.
  • Passion for using artificial intelligence tools to improve how the team builds, delivers, and scales.

About the Company

NVIDIA

Designs the chips and software that power AI

NVIDIA designs the chips and software that power artificial intelligence and sells complete data center systems to cloud providers, AI labs, and other businesses, along with GeForce graphics cards for gamers. Its GPUs, chips that perform thousands of calculations at once, became the main hardware for AI after researchers used them to train the AlexNet image-recognition model in 2012. NVIDIA connects many GPUs with its own processors and networking gear into servers and rack-sized computers, and developers program them through its CUDA software. These systems bring in most of its sales, and it also makes chips for cars and robots. Jensen Huang co-founded NVIDIA in 1993, and it is based in Santa Clara, California.

Company size
10,001+
Company stage
IPO
Headquarters
Santa Clara, CA
Founded
1993

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