Machine Learning Engineer

Role overview

Work as an individual-contributor Machine Learning Engineer developing intelligent capabilities for agreement and document workflows. The role combines applied research, natural language processing, deep learning, text mining, content understanding, and document processing to create more automated and personalized product experiences using contractual and legal data.

Responsibilities

- Research, test, and evaluate established and emerging NLP, machine learning, and deep learning methods for document and contract analysis. - Maintain and extend rule-based, supervised, and unsupervised language-processing systems. - Develop models for tasks such as named entity recognition, part-of-speech tagging, parsing, sentiment analysis, clustering, and text prediction. - Improve training-data maintenance, enrichment, and related model-development processes. - Deploy and support machine learning models in production using appropriate engineering and operational practices. - Work with product stakeholders to translate requirements into measurable, customer-agnostic machine learning success criteria.

Requirements

- At least five years designing, developing, deploying, and monitoring machine learning or deep learning solutions. - Professional programming experience with Python and one or more of C#, Java, C, or C++. - Experience with PyTorch, TensorFlow, spaCy, scikit-learn, or an equivalent machine learning framework. - Bachelor’s degree in computer science, physics, statistics, econometrics, operations research, applied mathematics, or a comparable computational discipline. - Understanding of model training, validation, testing, precision and recall, bias and variance, and related evaluation concepts. - Experience extracting, cleaning, and transforming large, diverse structured and unstructured datasets.

Nice to have

- Experience developing and deploying sequence-based deep learning models. - Familiarity with current large language model technologies, computer vision, or language-agnostic contract clause recognition. - Experience with the full software delivery lifecycle, including product architecture, code management, release processes, and production deployment. - A demonstrated commitment to following developments in machine learning and continuously expanding technical skills.

Benefits and work setup

- Hybrid arrangement with access to an office and a regular expectation of at least two days per week onsite, subject to team and business needs.