ML Engineer Intern

Unsiloed-Ai • San Francisco, CA

1d • Hybrid

About The Position

Unsiloed AI (YC F25) is the unstructured data interface for LLMs and AI agents. We build vision-first, layout-aware systems that use computer vision, OCR, and multimodal models to turn complex documents into deterministic, machine-readable representations. We are looking for a passionate and driven Machine Learning Engineer Intern to join our team. This is a research-heavy role, ideal for candidates who thrive on reading and implementing advanced research papers, devising innovative techniques, and working on cutting-edge AI challenges. You will play a pivotal role in building, training, fine-tuning, and deploying multi-modal models tailored for parsing complex unstructured data.

Requirements

• Currently pursuing or recently completed a degree in Computer Science, Data Science, or a related field with a focus on AI/ML.
• Demonstrated experience in academic research, projects, or publications related to AI/ML, particularly in the areas of multi-modal models and transformer-based architectures.
• Strong programming skills in Python and familiarity with ML frameworks such as TensorFlow or PyTorch.
• Experience with deploying machine learning models, including containerization using Docker and other MLOps tools (e.g., MLFlow, Kubernetes, or similar).
• Knowledge of end-to-end deployment pipelines.

Responsibilities

• Conduct in-depth research by reading academic papers and identifying state-of-the-art methods relevant to our domain.
• Build, train, and fine-tune multi-modal models to accurately extract and process information from unstructured data.
• Curate and manage custom datasets, ensuring they are tailored for training and benchmarking models.
• Experiment with and improve state-of-the-art machine learning and deep learning models to achieve higher accuracy and efficiency.
• Benchmark model performance against datasets, focusing on accuracy, efficiency, and robustness.

What This Job Offers

• Career Level: Intern
• Education Level: Associate degree