ML Engineer

We are seeking a Machine Learning Engineer with 3+ years of experience to design, deploy, and scale advanced NLP and computer vision models. If you excel at turning unstructured document data into production-ready AI solutions, we want to hear from you.

Responsibilities:

• Design, develop, and deploy ML models for document intelligence, content processing, and automated information extraction.
• Build end-to-end ML pipelines for processing unstructured text, images, scanned documents, and PDFs.
• Develop and improve NLP and CV solutions for document classification, entity extraction, semantic search, and document understanding.
• Experiment with deep learning architectures, Transformers, and LLM-based approaches to solve real-world document processing challenges.
• Convert research prototypes and experimental models into reliable, scalable, production-ready systems.
• Evaluate model performance using appropriate metrics, statistical analysis, benchmarking, and error analysis.
• Optimize model accuracy, inference speed, scalability, and resource utilization for production workloads.
• Build and maintain REST APIs and integrate ML models into existing product workflows.
• Own the deployment of production ML systems that redefine document infrastructure.
• Drive immediate product impact with $6.5M in Series A backing.
• Accelerate your career in a high-growth, remote-first engineering culture.
• Collaborate daily with a world-class, ambitious team of builders.

Requirements:

• 3+ years of hands-on experience in machine learning engineering, with a strong focus on computer vision, NLP, or document intelligence.
• Strong proficiency in Python, with experience writing production-quality, maintainable code.
• Practical experience building, training, evaluating, and deploying machine learning and deep learning models.
• Strong understanding of deep learning architectures, including CNNs and Transformers, along with fundamental ML algorithms, mathematics, and statistics.
• Experience with PyTorch and the Hugging Face Transformers ecosystem.
• Hands-on experience with unstructured text and document data, including text classification, information extraction, document understanding, or semantic search.