Lead Machine Learning Engineer

Role overview

Lead the development of production machine-learning capabilities for intelligent document and agreement workflows. This individual-contributor role spans the full AI/ML lifecycle, from research and model evaluation through deployment, monitoring, and continuous improvement, with a focus on natural-language processing, computer vision, and document understanding.

Responsibilities

- Research, develop, test, and evaluate deep-learning methods for complex document and contract-related problems. - Build NLP solutions for text representation, semantic retrieval, contextual extraction, understanding, and summarization. - Apply computer vision to document layout analysis, object detection, image classification, and tagging. - Improve model training, evaluation, online inference, performance metrics, and user-feedback mechanisms. - Partner with software engineers to deploy scalable models and reliable AI systems into production. - Translate user scenarios and product requirements into robust, customer-agnostic machine-learning designs.

Requirements

- 12+ years of related experience with a bachelor’s degree, 8+ years with a master’s degree, 5+ years with a PhD, or equivalent experience. - Experience designing, developing, deploying, and monitoring machine-learning or deep-learning solutions. - Strong Python programming skills and experience with PyTorch, TensorFlow, or an equivalent framework. - Bachelor’s degree in computer science, physics, statistics, econometrics, operations research, applied mathematics, or a comparable computational discipline. - Extensive experience collecting, cleaning, sampling, and processing large structured or unstructured datasets.

Nice to have

- Experience deploying large-scale document-understanding models in production. - Hands-on work combining computer vision and NLP in multimodal systems. - Knowledge of modern NLP techniques, including large language models and language representations. - Experience extracting text from OCR outputs and DOCX, image, or PDF sources.

Benefits and work setup

- Hybrid arrangement with access to an office and an expected minimum of two in-office days per week, subject to team requirements.