Infrastructure Engineer with ML Experience (Part-Time with likely full-time extension) for Restaurant Operation Assistant

Summary

• Experience in cross-platform frontend development (React, CSS, JS) for web applications
• Develop and deploy machine learning models into cloud-based environments (GCP, Firebase, Cloud Functions, Cloud Storage, Cloud Compute).
• Timezone: Europe
• Duration: 1-2 years
• Compensation: cash, equity/cash if long-term engagement
• Available to start ASAP

About the company Be part of an early-stage ML startup with high growth potential. Work on cutting-edge AI solutions impacting the restaurant and food supply chain industry. Potential equity-based compensation and long-term involvement as we scale. Gain experience in real-world ML deployment, cloud-based AI systems, and full-stack development. Job Role Integrate models with iOS applications (Swift) and web platforms (React, JavaScript, CSS). Develop deep learning models for natural language processing (NLP), named entity recognition (NER), and information extraction from menus. Build demand forecasting models using time-series techniques (ARIMA, DeepAR, Prophet, Transformer/LSTMs). Work on GraphML and graph databases (Neo4J) to enhance supply chain analytics. Develop and deploy machine learning models into cloud-based environments (GCP, Firebase, Cloud Functions, Cloud Storage, Cloud Compute). Design and implement web-based ML dashboards to visualize food inventory and demand trends. Develop APIs and microservices in Python and JavaScript for seamless backend integration. Connect the ML platform to POS systems (Square, Toast) to streamline restaurant operations.

Requirements Experience with deep learning for NLP and time-series forecasting. Background in graph databases and GraphML (preferred). Must HAVE experience with GCP Proficiency in React, Swift, and cross-platform development Interest in food supply chain management, restaurant tech, and predictive analytics. Comfortable working in a fast-paced startup with evolving priorities. Tech Stack ML/AI: Deep Learning (NER, NLP), Time-Series Forecasting (ARIMA, LSTMs, Prophet, DeepAR), Large Language Models (LLMs). Databases: Neo4J (GraphDB), Cloud Storage Solutions. Cloud: Google Cloud Platform (GCP), Firebase, Cloud Functions, Cloud Compute. Frontend: React (Web), Swift (iOS). Backend: Python, JavaScript (Microservices).