Senior Forward Deployed Engineer, Open Models
$184k - $357k • Remote • US, CA, Santa Clara
Posted 1d ago
Job Location
US, CA, Santa Clara
Tech Stack
Remote Work Policy
Fully remote
Categories
LLM Engineer
About the job
We are seeking a Senior Forward Deployed Engineer to lead technical engagements with partners and customers, focusing on NVIDIA's open model portfolio. The primary mission is to transform open models into production-ready AI solutions tailored to specific domains, covering the entire lifecycle from post-training to harness optimization. This role involves collaborating closely with NVIDIA's partner ecosystem, co-leading customer engagements, and developing resources that enable partners to scale their AI initiatives. You will work hands-on to build custom AI solutions, from initial customer conversations through to production deployment, ensuring models are optimized for performance and cost-effectiveness.
Responsibilities
- Drive AI solutions from discovery and scoping through post-training, harness optimization, and deployment, working with partners and customers.
- Design post-training and harness strategies, including methods, data, evaluation, and success criteria for each use case.
- Develop scalable architectures and deployment plans for production models.
- Build custom AI solutions across the full stack, including designing evaluations, creating synthetic data pipelines, and performing post-training with methods like LoRA and SFT.
- Tune the harness (prompting, tool use, orchestration, context management) for domain-specific model performance.
- 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 customers.
- Build reusable recipes, reference pipelines, and playbooks to enable partner success and ecosystem maturity.
- Connect NVIDIA's model research to partner and customer use cases, enhancing post-trainability and inference cost-effectiveness.
- Provide field insights back to product, research, and engineering teams for model and software stack improvements.
Requirements
- 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, applied AI, FDE, or solutions architecture.
- 3-5+ years of hands-on LLM post-training and model deployment experience.
- Solid fundamentals in deep learning and machine learning.
- Deep expertise in post-training methods (LoRA/PEFT, SFT, reinforcement learning) with an understanding of RL trade-offs.
- Strong grasp of model architectures and hyperparameters and their impact on 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/multi-node systems.
- Hands-on experience with post-training data, including synthetic data pipelines, dataset curation, 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 engagements, shape SOWs, and lead delivery with partners and customers.
- Excellent organizational skills, empathy for partner/customer needs, and ability to drive complex decisions across teams.
- Clear communication of complex technical ideas to diverse audiences.
- Strong problem-solving skills, intellectual curiosity, and commitment to partner/customer success.
Benefits
- Equity
- Benefits