Staff Software Engineer, Manufacturing Platform & AI

ApplyMint; Austin (TX); Hybrid; USD 207,000 - 662,000; Full time

Job summary

General Motors is seeking a Staff Software Engineer to own substantial areas of a production-critical platform used across manufacturing plants. You will work with Python/FastAPI, React/TypeScript, Java/Spring Boot, and cloud technologies to deliver scalable services, AI-enabled capabilities, and reliable integrations.

You will mentor engineers, shape technical direction, and collaborate with manufacturing and product leaders to ensure secure,observable, and maintainable systems in a hybrid work

Qualifications

• Bachelor’s degree in Computer Science, Computer Engineering, Software Engineering, or a related field.
• 8+ years of professional software engineering experience delivering production systems.
• Experience designing distributed applications, microservices, APIs, and event-driven systems.

Responsibilities

• Lead architecture and delivery of scalable software capabilities used by manufacturing teams across multiple plants.
• Design, build, test, and operate production-grade services across Python/ FastAPI, React/TypeScript, Java/Spring Boot, and cloud technologies.
• Mentor engineers, align dependencies, and drive engineering practices across teams.

Skills

Distributed systems

Python

FastAPI

React

TypeScript

Java

Spring Boot

PostgreSQL

Azure

Docker

Kubernetes

GitHub Actions

GitOps

Observability

CI/CD

Education

Bachelor's degree

Tools

Docker

Kubernetes

GitHub Actions

GitOps

Job description

General Motors is seeking a Staff Software Engineer to own substantial areas of a production-critical platform used across manufacturing plants. You will work with Python/FastAPI, React/TypeScript, Java/Spring Boot, and cloud technologies to deliver scalable services, AI-enabled capabilities, and reliable integrations.

You will mentor engineers, shape technical direction, and collaborate with manufacturing and product leaders to ensure secure,observable, and maintainable systems in a hybrid work