Senior Forward Deployed Engineer, Open Models - CA, Santa Clara, United States
$184,000 - $356,500 USD yearly
Originally posted 18 October 2026 by the employer.
Turn NVIDIA Nemotron and NVIDIA Cosmos into domain-specific production AI through LLM post-training and harness optimization.
About the role
This role drives technical engagement with partners and customers across NVIDIA's open model portfolio, including NVIDIA Nemotron and NVIDIA Cosmos, turning open models into production-ready AI tuned to specific domains from post-training through harness optimization.
What you'll do
- Drive AI solutions from discovery and scoping through post-training, harness optimization, and deployment alongside partners and customers. - Design evaluations, build synthetic data pipelines, and apply post-training methods including LoRA, parameter-efficient methods, supervised fine-tuning, and synchronous or asynchronous reinforcement learning. - Tune the harness through prompting, tool use, orchestration, and context management, while optimizing 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 to support independent partner success. - Connect model research to field use cases and collaborate with product, research, and engineering teams to integrate field insights back into Nemotron, Cosmos, and software stacks.
What you'll need
- 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. - Expertise in post-training methods including LoRA/PEFT, SFT, and reinforcement learning, with an understanding of trade-offs between synchronous and asynchronous RL approaches. - Understanding of large-scale distributed training and inference performance optimization, with the ability to tune workloads for memory efficiency, throughput, latency, and multi-GPU/multi-node scaling. - Hands-on experience with synthetic data pipelines, dataset curation and filtering, evaluation sets, and harness engineering for LLM and agentic systems. - Proficiency in PyTorch, Hugging Face Transformers, Python, and Linux.
Skills: forward deployed engineer, open models, post-training, harness optimization, LLM
This role has been open 0 days — well below the 52-day median for Infrastructure/Platform Software roles.
Infrastructure/Platform Software · Infrastructure Platform
|
Open roles in category
968
|
Median days open
52 d
|
Median salary
$236k
|
See the full market breakdown ▾Category comparison, skills in demand, and who else is hiring
| Metric | NVIDIA | All employers we track in this specialty (968 roles · 86 employers) |
|---|---|---|
| Open roles in this specialty | 286 | 968 |
| Open roles in the wider Software, Firmware & Systems family | 984 | 6166 · 145 employers |
| Median days open | 46 d | 52 d (−6 d vs this employer) |
| Median salary (USD postings) | — | $236k |
Skills observed across this category: forward deployed engineer, open models, post-training, harness optimization, LLM
Who's hiring in this category
- NVIDIA (this employer) · 278 open roles · median 47 d
- Qualcomm · 62 open roles · median 54 d
- AMD · 54 open roles · median 20 d
- Graphcore · 44 open roles · median 94 d
- Intel Corporation · 43 open roles · median 13 d
- Cerebras · 36 open roles · median 84 d
How we counted: 968 open Infrastructure/Platform Software (Infrastructure Platform) roles from 86 employers tracked in the SemiconductorJobs index, counted 19 Oct 2026. Specialty figures count only roles carrying this exact specialty label, so an employer's related work in neighbouring specialties is not included there — it is counted in the wider Software, Firmware & Systems family row. Figures refresh nightly.