About the job
About Apiphany
Apiphany is a cutting-edge AI company dedicated to advancing physical product development. We enable innovators across sectors such as automotive, aerospace, medtech, and energy to convert vast amounts of unstructured technical data into real-time, actionable insights. Supported by prominent investors including Markforged, Databricks, GM, and Character, our mission is to redefine engineering decision-making, transforming complexity into clarity for leading manufacturers.
Our models are tailored to address the intricacies of engineering and manufacturing, comprehending physics principles, design specifications, and programming constraints. We are a compact, elite team of builders from Stanford, Berkeley, MIT, UW, and CMU, complemented by industry veterans from GM, Ford, and Genesis Therapeutics. Together, we are passionate about revolutionizing hard-tech and establishing a category-defining enterprise.
About the Role
- Develop AI-powered manufacturing applications:
Create full-stack solutions that seamlessly integrate real-time AI models for predictive maintenance, anomaly detection, defect classification, and process optimization. - Engineer for complexity and scale:
Design and implement resilient, distributed systems capable of managing large-scale data pipelines, streaming industrial sensor data, and real-time AI inference. - Integrate with factory-floor systems:
Collaborate with IIoT, MES, SCADA, PLCs, and edge computing to deploy AI models that engage with manufacturing processes instantaneously. - Bridge AI and human decision-making:
Craft intuitive, high-performance interfaces that empower operators, engineers, and managers to interpret AI-driven insights and take decisive action. - Own the full-stack:
Build and optimize front-end applications (React, TypeScript, etc.) and backend services (Python, Node.js, or similar), ensuring cohesive end-to-end experiences. - Ship in real-world production environments:
Deploy software that operates in factories, on edge devices, or in the cloud, adhering to 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 metrics.

