Software Engineer

Ford Ford · Auto · Dearborn, MI +1 · Manufacturing Engineering

Software Engineer to build the software ecosystem for AI Vision Systems, focusing on edge software integration, full-stack API development, data architecture, and web interfaces, while leveraging AI tools for development.

What you'd actually do

  1. Develop and optimize software to deploy machine learning models on edge devices (NVIDIA Jetson/Thor), ensuring low-latency performance for real-time vision tasks.
  2. Build scalable RESTful APIs and microservices (Python/C++) that allow edge devices to communicate seamlessly with cloud backends.
  3. Design and manage data pipelines using Google Cloud tools (BigQuery, Postgres) to handle real-time image/video data and model telemetry.
  4. Create intuitive, high-performance web-based dashboards (React/TypeScript) for monitoring system health and visualizing AI-driven insights.
  5. Heavily leverage Agentic AI tools and LLM-assisted workflows to accelerate development cycles and maintain high code quality.

Skills

Required

  • 3+ years of professional software engineering experience in a production environment
  • Proven experience deploying software to edge computing hardware or IoT devices
  • Strong proficiency in Python
  • Experience building on Google Cloud Platform (GCP) or similar (AWS/Azure), specifically with managed database services
  • Experience building responsive web applications with React or similar modern frameworks
  • Familiarity using docker as the key configuration, build, and deploy mechanism, CI/CD pipelines and disciplined version control approach (GIT based)

Nice to have

  • Experience with OpenCV, TensorRT, or OpenVINO for vision optimization
  • Familiarity with ML frameworks like PyTorch or TensorFlow
  • Knowledge of industrial protocols (MQTT, WebSockets) for real-time data streaming
  • A passion for "Agentic" workflows and continuous improvement

What the JD emphasized

  • low-latency performance
  • real-time vision tasks
  • real-time image/video data
  • Agentic AI tools
  • LLM-assisted workflows

Other signals

  • Deploying ML models on edge devices
  • Building software ecosystem for AI Vision Systems
  • Integrating edge hardware with cloud backends