Senior Forward Deployed Engineer, Open Models

About the role

NVIDIA is hiring a Senior Forward Deployed Engineer to drive technical engagement with partners and customers across its open model portfolio, including NVIDIA Nemotron and NVIDIA Cosmos. The goal is to turn open models into production-ready AI tuned to a specific domain, from post-training through harness optimization. The role works mainly through NVIDIA's partner ecosystem, alongside ecosystem engineering and research teams, co-leading joint customer engagements and building recipes, reference pipelines and playbooks that help partners succeed.

Key responsibilities

• Drive AI solutions from discovery and scoping through post-training, harness optimization and deployment, working with partners and customers.
• Design evaluations, build synthetic data pipelines, and perform post-training with LoRA and other parameter-efficient methods, supervised fine-tuning and reinforcement learning.
• Make architecture and hyperparameter choices, tune the harness (prompting, tool use, orchestration, context management) and optimize inference for cost-effective production use.
• Lead technical engagements by scoping work, shaping statements of work and driving delivery with partner engineering teams and joint customers.
• Build reusable recipes, reference pipelines and playbooks, and feed field insights back into Nemotron, Cosmos and the software stack.

Required profile

• Bachelor's, Master's or PhD in Computer Science, Engineering, Machine Learning or a related field, or equivalent 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.
• Proven ability to scope an engagement, shape an SOW and lead delivery with partners and customers.
• Clear communication of complex technical ideas to engineers and C-level executives, strong problem-solving and organizational skills.

Required skills

• Deep expertise in post-training methods: LoRA/PEFT, SFT and reinforcement learning (synchronous and asynchronous).
• Understanding of large-scale distributed training and inference optimization, including multi-GPU and multi-node systems.
• Hands-on experience with synthetic data pipelines, dataset curation, filtering and evaluation sets, and harness engineering for LLM and agentic systems.
• Proficiency in PyTorch, Hugging Face Transformers, Python and Linux.
• Experience with multimodal or world-foundation-model post-training and training jobs on large GPU clusters with SLURM or Kubernetes is a plus.

What we offer

• Base salary of 184,000 to 287,500 USD for Level 4 and 224,000 to 356,500 USD for Level 5, depending on location, experience and market pay.
• Eligibility for equity and benefits.