# Senior Forward Deployed Engineer, Open Models at NVIDIA

NVIDIA is hiring a Senior Forward Deployed Engineer, Open Models in Santa Clara, United States. It pays $184k-$288k a year and Level rates it Builds AI ●●●●; you can [apply on Level](https://jobsbylevel.com/go/af284962-b96a-45ef-bf74-ced2628d9c16?utm_medium=ai-agent).

AI Level 4, AI centrality 98 out of 100. US, CA, Santa Clara.

## Details

- Company: [NVIDIA](https://jobsbylevel.com/companies/nvidia)
- AI level: AI Level 4 (score 98 out of 100)
- Location: US, CA, Santa Clara
- Salary: $184k-$288k
- Posted: October 18, 2026
- Apply: https://jobsbylevel.com/go/af284962-b96a-45ef-bf74-ced2628d9c16?utm_medium=ai-agent

## Prepare for this job

The job page lists 5 likely interview questions for this offer, with a CV checklist: https://jobsbylevel.com/jobs/senior-forward-deployed-engineer-open-models-at-nvidia-cf2ebb

## Description

Do you want to help the world build custom AI? 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. Are you one of the strongest post-training applied researchers you know? Ready to take a model from a first customer conversation all the way to production? We'd love to meet you. What you'll be doing: This role drives AI solutions from discovery and scoping through post-training, harness optimization, and deployment, working alongside partners and customers. Each use case becomes a post-training and harness strategy with the right methods, data, evaluation, and success criteria. Scalable architectures and deployment plans then take the model to production. The work is hands-on, as we build together with the ecosystem. Custom AI takes skill across the full stack, and we work across all of it: designing evaluations, building synthetic data pipelines, and post-training with LoRA and other parameter-efficient methods, supervised fine-tuning, and reinforcement learning (synchronous and asynchronous). It also means making the architecture and hyperparameter choices that improve results. We tune the harness (prompting, tool use, orchestration, and context management) so the model shines in a specific domain, and we optimize inference so it runs cost-effectively in production. You will lead technical engagements by scoping the work, shaping statements of work, and driving delivery with partner engineering teams and joint customers. We scale our impact by building reusable recipes, reference pipelines, and playbooks that let partners succeed independently and help the ecosystem mature in building domain-specific AI. We connect NVIDIA's model research to partner and customer use cases, unlocking the post-trainability and inference cost-effectiveness of our models, and work with our product, research, and engineering teams to bring field insights back into Nemotron, Cosmos, and our software stack. Above all, we're excited to help organizations worldwide build, own, and run their own AI on open source models! What we need to see: 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,

The description is cut here. Read the full offer: https://jobsbylevel.com/jobs/senior-forward-deployed-engineer-open-models-at-nvidia-cf2ebb

Source: https://jobsbylevel.com/jobs/senior-forward-deployed-engineer-open-models-at-nvidia-cf2ebb

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