AI Infrastructure Software Engineer

The Mice Groups, Inc.; Livermore (CA); On-site; USD 103,000 - 124,000; Full time

Job summary

The Mice Groups, Inc. is seeking an AI Infrastructure Software Engineer for a contract-to-hire engagement in the SF Bay Area (Tri-Valley). Local candidates preferred.

Role emphasizes production-grade software in Python/Go/C, AI/ML model deployment, and robust CI/CD pipelines in Linux/container environments. Ideal candidates have 5–7 years of software development, experience with NVIDIA AI stack, MLOps workflows, and strong networking fundamentals in data-center contexts.

Qualifications

• 5-7 years of professional software development in Python, Go, or C (at least one at a strong, production level).
• Experience with AI/ML development—training, serving, and integration into apps.
• Hands-on CI/CD experience with automated testing and release pipelines (GitHub Actions, GitLab CI, Jenkins).
• Comfortable in Linux environments, containers, and git-based workflows.
• Data center networking domain experience (switching/routing, fabric design, RDMA/RoCE, InfiniBand).
• NVIDIA AI software stack (CUDA, NCCL, DOCA, cuDNN, Triton Inference Server).
• MLOps experience—model deployment, versioning, monitoring, retraining workflows.

Skills

Python

Go

C

CI/CD

Linux

Containers

ML concepts

CUDA

cuDNN

Triton

RDMA

Networking

Tools

GitHub Actions

GitLab CI

Jenkins

Docker

Job description

Our client, a stealth startup developing AI-powered solutions is seeking an AI Infrastructure Software Engineer for a contract-to-hire opportunity.

Candidates local to the SF Bay Area will be considered.

Location is the Tri-Valley area

Contract-to-hire 6-12months to FTE

Hourly Rate pays between $75-$90/hr.

Requirements

• 5 -7 years of professional software development in Python, Go, or C (at least one at a strong, production level).
• Working knowledge of AI/ML development — how models are trained, served, and integrated into applications.
• Hands-on CI/CD experience (GitHub Actions, GitLab CI, Jenkins, or equivalent), including automated testing and release pipelines.
• Comfortable in Linux environments, containers, and git-based workflows.
• Able to work independently in an early-stage environment with limited process and shifting priorities.
• Data center networking domain experience — switching/routing, fabric design, RDMA/RoCE, InfiniBand, or high-performance interconnects.
• NVIDIA AI software stack: CUDA, NCCL, DOCA, cuDNN, Triton Inference Server, or NVIDIA networking libraries.
• MLOps experience — model deployment, versioning, monitoring, and retraining workflows.