NVIDIA
Senior Forward Deployed Engineer, Open Models
$184K–$356K
US, CA, Santa Clara · Senior · Full-time · Posted Oct 18, 2026
- Experience
- 8+ years in ML or software engineering, applied AI, FDE, or solutions architecture, including 3–5+ years of hands-on LLM post-training and taking models to production.
- Education
- Bachelor's, Master's, or PhD in Computer Science, Engineering, Machine Learning, or a related field, or equivalent experience.
About the job
Leads partner- and customer-facing engagements to post-train open models, optimize their harnesses and inference, and deploy domain-specific AI solutions in production.
What you'll do
- Scope engagements, shape statements of work, and lead delivery with partners and customers.
- Develop post-training strategies, data and evaluation pipelines, harness optimizations, and production deployment plans.
- Create reusable recipes and playbooks, and relay field insights to NVIDIA product, research, and engineering teams.
What they ask for
- Deep expertise in LLM post-training methods, including LoRA/PEFT, SFT, and reinforcement learning.
- Experience with distributed training and inference optimization across multi-GPU and multi-node systems; post-training data and harness engineering.
- Proficiency in PyTorch, Hugging Face Transformers, Python, and Linux.
- Ability to scope engagements, shape SOWs, lead delivery, and communicate technical concepts to varied audiences.
Nice to have
- Hands-on experience with Nemotron, Cosmos, NVIDIA technologies, NeMo, Megatron, or post-training platforms such as Tinker APIs.
- Experience with multimodal or world-foundation-model post-training, large GPU clusters, SLURM, or Kubernetes.
Pay
184,000 USD - 287,500 USD (base salary, Level 4); 224,000 USD - 356,500 USD (base salary, Level 5)
Tools
LoRA/PEFT, PyTorch, Hugging Face Transformers, Python, Linux, NVIDIA NeMo, Megatron, SLURM
AI focus
Post-training and production deployment of domain-specific open models, including synthetic data, evaluations, reinforcement learning, harness optimization, and inference efficiency.
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