Tech Stack
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
AI @ 4
API @ 4
Audit @ 3
Communication @ 6
Compliance @ 3
GPU
LLM @ 7
Observability
RAG @ 4
Reinforcement Learning @ 4
vLLM @ 7
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
NVIDIA is building the next era of computing through AI, accelerated computing, and GPU technology. The NIM team is developing a model customization and deployment lifecycle platform in Santa Clara, California, enabling ISVs and CSPs to take models through selection, fine-tuning, evaluation, deployment, and compliance.
Responsibilities
- Build the fine-tuning handoff pipeline, including LoRA adapter repackaging, re-quantization, and re-validation into NIM.
- Develop an evaluation harness to ensure models meet high standards.
- Implement observability and attestation layers that produce auditable compliance artifacts.
- Partner closely with ISVs and CSPs to roll out NVIDIA NIMs at large scale.
- Define and improve durable platform APIs while avoiding one-off integrations.
- Ensure projects are completed accurately through strict attention to detail and proven methodologies.
Requirements
- Bachelor's degree in Computer Science, Engineering, or equivalent experience.
- Experience with model customization tools and techniques, including LoRA, QLoRA, supervised fine-tuning (SFT), reinforcement learning (RL), preference optimization such as DPO, and retrieval-augmented generation (RAG).
- At least 7 years of experience with LLM serving infrastructure such as vLLM, TRT-LLM, or equivalent.
- Deep understanding of model quantization and fine-tuning workflows.
- Demonstrated experience developing robust platform APIs.
- Strong collaboration skills and experience working in cross-functional teams.
- Outstanding problem-solving skills and meticulous attention to detail.
Preferred Qualifications
- Experience with large-scale deployment of AI models.
- Familiarity with compliance and audit processes for AI models.
- Contributions to open-source AI projects.
- Strong publication record in relevant fields.
- Excellent communication skills and a passion for mentorship.
Compensation And Benefits
- Base salary for Level 4: USD 184,000–287,500 per year.
- Base salary for Level 5: USD 224,000–356,500 per year.
- Eligibility for equity and benefits.
- Applications will be accepted at least until August 30, 2026.
- NVIDIA is an equal opportunity employer.
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