Senior Forward Deployed Engineer, Open Models

About the role

We're hiring a Senior Forward Deployed Engineer to drive technical engagement with partners and customers across NVIDIA's open model portfolio, including NVIDIA Nemotron and NVIDIA Cosmos. The mission is to turn open models into production-ready AI tuned to a specific domain, from post-training through harness optimization. We want any organization to build, own, and run its own AI on open source models, and this role helps the ecosystem get there at scale. Unlike most forward deployed roles, our work runs primarily through NVIDIA's partner ecosystem. Our FDEs work side by side with top ecosystem engineering and research teams, co-lead joint customer engagements, and build the recipes and playbooks that help partners succeed and scale. We also work directly with customers when the work calls for it.

Responsibilities

• Drive AI solutions from discovery and scoping through post-training, harness optimization, and deployment, working alongside partners and customers
• Design evaluations, build synthetic data pipelines, and execute post-training with LoRA and other parameter-efficient methods, supervised fine-tuning, and reinforcement learning (synchronous and asynchronous)
• Make architecture and hyperparameter choices that improve training outcomes
• Tune the harness (prompting, tool use, orchestration, and context management) so the model shines in a specific domain
• Optimize inference for cost-effective production deployment
• 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 that let partners succeed independently and help the ecosystem mature in building domain-specific AI
• Connect NVIDIA's model research to partner and customer use cases, unlocking post-trainability and inference cost-effectiveness of models
• Work with product, research, and engineering teams to bring field insights back into Nemotron, Cosmos, and the software stack

Requirements

• 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
• Solid fundamentals in deep learning and machine learning
• Deep expertise in post-training methods, including LoRA/PEFT, SFT, and reinforcement learning, with an understanding of the trade-offs between RL synchronous and asynchronous approaches
• Strong grasp of model architectures and hyperparameters and how they shape training outcomes
• Good understanding of large-scale distributed training and inference performance optimization
• Ability to configure and tune workloads for memory efficiency, throughput, latency, and scaling across multi-GPU and multi-node systems
• Hands-on experience with post-training data, including synthetic data pipelines, dataset curation and filtering, and evaluation sets
• Experience with harness engineering for LLM and agentic systems
• Proficiency in PyTorch, Hugging Face Transformers, Python, and Linux
• Proven ability to scope an engagement, shape an SOW, and lead delivery with partners and customers
• Excellent organizational skills, empathy for partner and customer needs, and the ability to drive complex decisions across teams and companies through trusted relationships
• Clear communication of complex technical ideas, from engineers to C-level executives
• Strong problem-solving skills, intellectual curiosity, and a commitment to partner and customer success

Qualifications

• Hands-on technical expert and problem solver who can clearly describe solution paths for complex challenges
• History of enabling partners and customers to successfully adopt open models and NVIDIA technologies and platforms
• Technical vision that influences product and engineering decisions and drives production outcomes
• Hands-on experience with Nemotron, Cosmos, and other open models, as well as both serverless and self-managed post-training platforms (e.g., Tinker APIs), including open-source frameworks such as NVIDIA NeMo and Megatron
• Experience with multimodal or world-foundation-model post-training, and running training jobs on large GPU clusters with SLURM or Kubernetes

Pay

Base salary range is 184,000 USD – 287,500 USD for Level 4, and 224,000 USD – 356,500 USD for Level 5, determined by location, experience, and pay of employees in similar positions. Eligible for equity and benefits.

Applications accepted at least until October 22, 2026. Job ID: JR2028008.