Senior Forward Deployed Engineer, Open Models

NVIDIA Corporation; United States; On-site; USD 180,000 - 240,000; Full time

Job summary

NVIDIA Corporation seeks a Senior Forward Deployed Engineer to drive technical engagements with partners and customers across its open model portfolio, including Nemotron and Cosmos. The role focuses on turning open models into production-ready AI tailored to domains, covering post-training through harness optimization.

You will co-lead customer engagements, craft reusable recipes and playbooks, and collaborate directly with customers to scale production AI across industries.

Qualifications

• Bachelor's, Master's, or PhD in Computer Science, Engineering, Machine Learning, or related field or equivalent experience.
• 8+ years in ML or software engineering with 3-5+ years in hands-on LLM post-training and production.
• Deep learning fundamentals; expertise in LoRA/PEFT, SFT, RL; understanding RL trade-offs.
• Experience with distributed training, memory optimization, multi-GPU/multi-node scaling.

Responsibilities

• Lead technical engagements by scoping work and shaping statements of work.
• Drive delivery with partner engineering teams and joint customers.
• Develop reusable recipes, reference pipelines, and playbooks for ecosystem adoption.
• Bridge research and product teams to apply field insights to models like Nemotron and Cosmos.

Skills

LLM post-training

Python

PyTorch

Hugging Face Transformers

Linux

Distributed training optimization

Cross-team leadership

SOW & engagement scoping

Education

Bachelor's/Master's/PhD in CS/Engineering/ML or related

Tools

NVIDIA Nemotron

NVIDIA Cosmos

Tinker APIs

NeMo & Megatron framework

Job 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 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, 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.

Ways to Stand Out from the Crowd:

• A 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.
• A 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.

Ex