Senior Software Engineer, AI Model Lifecycle Platform

NVIDIA; Santa Clara (CA); On-site; USD 184,000 - 357,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA is seeking a Senior Software Engineer for the NIM Tools team in Santa Clara, CA. You will build a scalable model customization and deployment lifecycle platform used by ISVs and CSPs worldwide.

Lead API design, end-to-end model workflows from selection to fine-tuning, evaluation, deployment and audit artifacts, and collaborate across teams to deliver robust solutions with strong debugging and mentorship capabilities.

Qualifications

• Bachelor's degree in Computer Science, Engineering, or equivalent experience.
• 7+ years of experience with LLM serving infrastructure (vLLM, TRT-LLM, or equivalent).
• Deep understanding of model quantization and fine-tuning workflows.
• Demonstrated history of developing robust platform APIs.
• Strong collaborative skills and experience in cross-functional teams.

Responsibilities

• Compose and build the fine-tuning handoff pipeline, including LoRA adapter repackaging, re-quantization, and re-validation into NIM.
• Develop the evaluation harness, ensuring models meet our high standards.
• Implement the observability and attestation layer to produce auditable compliance artifacts.
• Work with ISVs and CSPs to roll out NVIDIA NIMs at scale.
• Define and improve durable platform APIs, avoiding one-off integrations.
• Ensure flawless completion of projects through attention to detail and proven methodologies.

Skills

LLM serving infrastructure

Model quantization and fine-tuning

Platform APIs development

Cross-functional collaboration

Mentorship and communication

Education

Bachelor's degree in Computer Science or Engineering

Job description

NVIDIA is seeking a Senior Software Engineer for the NIM Tools team in Santa Clara, CA. You will build a scalable model customization and deployment lifecycle platform used by ISVs and CSPs worldwide.

Lead API design, end-to-end model workflows from selection to fine-tuning, evaluation, deployment and audit artifacts, and collaborate across teams to deliver robust solutions with strong debugging and mentorship capabilities.