Software Engineer — AI Infrastructure & Networking

Vikara AI; Pleasanton (CA); On-site; USD 120,000 - 180,000; Full time

Job summary

Vikara AI is seeking a hands-on software engineer to design and build core components for an emerging AI platform. You will work closely with the founding team, shaping architecture, implementing features, testing, deploying, and operating critical systems in a fast-paced startup setting.

Candidate should have 3–5 years of software development experience and be comfortable working in person in Pleasanton, CA, with strong problem-solving skills and a passion for scalable, reliable infrastructure.

Qualifications

• Requires 3–5 years of professional software development experience.
• Proficiency in Python, C/C++, or Go.
• Ability to work in person in Pleasanton, CA.
• Strong problem-solving skills in a startup environment.

Responsibilities

• Design and build core software components for an emerging AI platform.
• Collaborate with the founding team to manage the full engineering lifecycle: architecture, implementation, testing, deployment, and operations.

Skills

Python

C++

Go

AI Infrastructure

Data-center Networking

Distributed Systems

PostgreSQL

CI/CD

Telemetry

Machine Learning

Observability

Network Automation

GPU-accelerated Computing

MLOps

Containerization

Cloud-native Infrastructure

Job description

Design and build core software components for an emerging AI platform while collaborating with the founding team. Manage the full engineering lifecycle including architecture, implementation, testing, deployment, and operations.

Requirements:

Requires 3-5 years of professional software development experience with proficiency in Python, C/C++, or Go. Candidates must be able to work in person in Pleasanton and possess strong problem-solving skills in a startup environment.

Key Skills:

• Python
• C++
• Go
• AI Infrastructure
• Data-center Networking
• Distributed Systems
• PostgreSQL
• CI/CD
• Telemetry
• Machine Learning
• Observability
• Network Automation
• GPU-accelerated Computing
• MLOps
• Containerization
• Cloud-native Infrastructure