Highlights
Responsibilities: Edge Software Integration: Develop and optimize software to deploy machine learning models on edge devices (NVIDIA Jetson/Thor), ensuring low-latency performance for real-time vision tasks. Full-Stack API Development: Build scalable RESTful APIs and microservices (Python/C++) that allow edge devices to communicate seamlessly with cloud backends.
Numbers & Facts
| Location | Palo Alto, CA |
Description
About the Role:
We are seeking an experienced Full-Stack Software Engineer to build the software ecosystem powering our next-generation AI Vision Systems. You will develop the "connective tissue" between high-performance machine learning models running on edge hardware and our Google Cloud-based analytics backend. This is a hands-on role for an engineer who is passionate about bringing AI out of the lab and into the real world.
Want to come work on the development of cutting edge solutions in manufacturing intelligence? At Ford, we are re-imagining how manufacturing looks in the age of AI and building real solutions to implement in our factories to solve real problems and redefine what World Class looks like. We are looking for highly motivated professionals who want to function as a team to build great products, solve difficult problems, and set a new level of excellence.
Responsibilities:
- Edge Software Integration: Develop and optimize software to deploy machine learning models on edge devices (NVIDIA Jetson/Thor), ensuring low-latency performance for real-time vision tasks.
- Full-Stack API Development: Build scalable RESTful APIs and microservices (Python/C++) that allow edge devices to communicate seamlessly with cloud backends.
- Data Architecture: Design and manage data pipelines using Google Cloud tools (BigQuery, Postgres) to handle real-time image/video data and model telemetry.
- Web Interfaces: Create intuitive, high-performance web-based dashboards (React/TypeScript) for monitoring system health and visualizing AI-driven insights.
- AI-Augmented Engineering: Heavily leverage Agentic AI tools and LLM-assisted workflows to accelerate development cycles and maintain high code quality.
- Cross-Functional Delivery: Collaborate with Data Scientists to containerize models (Docker/Kubernetes) and with Hardware Engineers to validate performance on the factory floor.
Responsibilities:
- Edge Software Integration: Develop and optimize software to deploy machine learning models on edge devices (NVIDIA Jetson/Thor), ensuring low-latency performance for real-time vision tasks.
- Full-Stack API Development: Build scalable RESTful APIs and microservices (Python/C++) that allow edge devices to communicate seamlessly with cloud backends.
- Data Architecture: Design and manage data pipelines using Google Cloud tools (BigQuery, Postgres) to handle real-time image/video data and model telemetry.
- Web Interfaces: Create intuitive, high-performance web-based dashboards (React/TypeScript) for monitoring system health and visualizing AI-driven insights.
- AI-Augmented Engineering: Heavily leverage Agentic AI tools and LLM-assisted workflows to accelerate development cycles and maintain high code quality.
- Cross-Functional Delivery: Collaborate with Data Scientists to containerize models (Docker/Kubernetes) and with Hardware Engineers to validate performance on the factory floor.