Full Stack Engineers

Build full-stack solutions that integrate Real Time AI (Artificial Intelligence) models for predictive maintenance, anomaly detection, defect classification, and process optimization.

• Engineer for complexity and scale: Design and implement robust, distributed systems that handle large-scale data pipelines, streaming industrial sensor data, and Real Time AI inference.

• Integrate with factory-floor systems: Work with Industrial Internet of Thing (IIoT), Manufacturing Execution System (MES), Supervisory Control and Data Acquisition (SCADA), Programmable Logic Controls (PLCs), and edge computing to deploy AI models that interact with manufacturing processes in real time.

• Bridge AI and human decision-making: Develop intuitive, high-performance interfaces that allow operators, engineers, and managers to interpret AI-driven insights and take action.

• Own the full-stack: Build and optimize Front End applications (React, TypeScript, etc.) and Back End services (Python, Node.js, or similar), ensuring seamless end-to-end experiences.

• Ship in real-world production environments: Deploy software that runs in factories, on edge devices, or in the cloud, working within the constraints of industrial infrastructure.

• Iterate quickly in a high-uncertainty domain: Prototype, test, and refine solutions based on direct user feedback and real-world performance data.

Bachelor's degree in computer science, information technology, software engineering, or a related field. One year of experience as a Software Engineer or Developer or related that includes the following:

1. 0-to-1 product development in building and shipping software from conception, navigating uncertainty and scaling from Minimum Viable Product (MVP) to production.
2. Full-stack engineering, including Front End frameworks (React, TypeScript, or similar) and Back End development (Python, Node.js, or similar).
3. Industrial automation, predictive maintenance, process optimization, quality control, or smart factory technologies.
4. AI/ML deployment, including integrating ML models into production applications using tools such as TensorFlow, PyTorch, or ONNX.
5. Working with industrial data, including time-series sensor data, machine telemetry, and Real Time control systems.
6. Cloud and edge computing, including deploying applications on AWS, GCP, or Azure, and running AI models on edge devices.

Apiphany is a pioneering foundational AI company for physical product development. We empower global innovators in automotive, aerospace, medtech, and energy to transform mountains of unstructured technical data into real-time, actionable insights. Backed by world-class investors from Markforged, Databricks, GM, and Character, our mission is to revolutionize how engineering decisions are made, turning complexity into clarity for the world's top manufacturers. Our models are built for the complexities of engineering and manufacturing. Our models understand physics principles, design specifications, and program constraints. We're a small, elite team of builders from Stanford, Berkeley, MIT, UW, and CMU, alongside industry leaders from GM, Ford, and Genesis Therapeutics. We're passionate about transforming hard-tech and building a category-defining company together.