AI/ML Engineer Intern

Company Description Kastack Labs is driven by innovation and passion, providing solutions that empower businesses to streamline operations and boost productivity. As a forward-thinking company, we embrace young talent and foster a culture of continuous learning and collaboration. Our mission is to make an impact through efficiency and forward-looking strategies.

Join us to play a pivotal role in shaping the future of operations management in a supportive, dynamic, and growth-oriented environment. *** THIS IS AN UNPAID INTERNSHIP ROLE **** Role Description We are seeking an AI/ML Engineer Intern for a remote internship role. The intern will be responsible for designing, implementing, and optimizing machine learning models to solve complex challenges. Key responsibilities will include data preprocessing, model training, testing, and deployment.

Work on deploying and optimizing ML models directly on mobile devices. Focus on performance, latency, and privacy-first AI. Qualifications Strong Python fundamentals Hands-on with TensorFlow / PyTorch , llama.cpp Understanding of model optimization (quantization, pruning) Basic knowledge of LLMs / embeddings Responsibilities Convert and deploy models using TensorFlow Lite / ONNX / Core ML Quantize and optimize models for low memory + battery usage Benchmark latency, memory, and inference performance Work on on-device NLP tasks (summarization, embeddings, intent detection) Collaborate with mobile team for real-world deployment constraints You can make models run fast on weak hardware You understand trade-offs (accuracy vs performance) Nice to Have Experience with TFLite / Core ML / ONNX Exposure to edge AI use cases Benefits Direct exposure to building real-world AI systems (LLMs, memory, personalization) Opportunity to work on cutting-edge conversational AI and hybrid models Hands-on experience with embeddings, retrieval, and AI workflows High learning environment with real ownership and experimentation Flexible, remote work structure Potential opportunity for a full-time role based on performance