Machine Learning Engineer

Location: Dublin, Ireland

Department: Engineering

Work Mode: Hybrid

About the Role

DocuSign brings agreements to life, empowering over 1.5 million customers and a billion people across 180 countries to accelerate business and simplify lives through our Intelligent Agreement Management platform. As the #1 company in e-signature and contract lifecycle management (CLM), we unleash trapped data from documents, connecting it to core business systems to save time, money, and unlock opportunities. Join our innovative Engineering team as a Senior Machine Learning Engineer, an individual contributor role reporting to the Director of Engineering, where you'll pioneer ML solutions driving personalized, automated experiences in e-signature and agreement automation. In this hybrid role (minimum 2 days/week in-office), you'll collaborate with our ML team to research and apply cutting-edge NLP, ML, and DL technologies to contractual and legal documents. Prototype models leveraging deep learning, content understanding, and document processing to enhance DocuSign's platform. Responsibilities include extending rule-based and unsupervised NLP methods, tackling tasks like Named Entity Recognition, sentiment analysis, and text prediction, while deploying production-ready models. You'll deepen your understanding of our end-to-end development processes and partner with Product Management to translate requirements into scalable success metrics, all while advancing language-agnostic clause recognition for global languages including Cyrillic and double-byte scripts. We're seeking a passionate innovator with 5+ years in ML/DL solutions, Python expertise in PyTorch/TensorFlow/spaCy, and a computational degree. Preferred experience includes sequence models, BERT/GPT-style NLP, computer vision, and staying ahead of trends. At DocuSign, we foster an equal opportunity culture of trust, openness, and continuous learning, where your contributions make the world more agreeable. Feel pride in building industry-leading solutions that transform how businesses manage agreements—apply today to be part of our mission. #LI-Hybrid #LI-IC1

Key Responsibilities

• Collaborate with the ML team to research, test, and evaluate emerging NLP/ML/DL methods for contractual and legal document processing
• Apply NLP techniques to maintain and extend rule-based, supervised, and unsupervised methods in DocuSign's Intelligent Agreement Management platform
• Develop and apply ML/DL algorithms to NLP tasks including Named Entity Recognition, POS tagging, Parsing, Sentiment Analysis, Clustering, and text prediction
• Prototype machine learning models to enable personalized and automated customer experiences in e-signature and CLM solutions
• Gain deep understanding of DocuSign's product architecture, software development lifecycle, code management, and release processes
• Improve existing training data maintenance and enrichment processes for scalable ML operations
• Deploy ML models to production using current and emerging technologies within the Machine Learning team
• Partner closely with Product Management to define robust, customer-agnostic success metrics from product requirements
• Leverage deep learning, content understanding, and document processing to unlock business-critical data from agreements
• Contribute to language-agnostic contract clause recognition across diverse global languages

Required Qualifications

• 5+ years of experience in designing, developing, deploying, and monitoring machine learning and deep learning solutions
• Strong experience programming in Python with ML frameworks such as PyTorch, TensorFlow, spaCy, or scikit-learn
• Proficiency in one or more programming languages including Python, C#, Java, or C/C++
• Bachelor’s degree in Computer Science, Physics, Statistics, Econometrics, Operations Research, Applied Mathematics, or equivalent computational field
• Demonstrated ability to work with large-scale structured and unstructured data extraction, cleansing, and manipulation
• Experience with end-to-end ML lifecycle including training, validation, testing, precision/recall, and bias/variance analysis
• Hands-on experience deploying ML models to production environments

Preferred Qualifications

• Experience developing and deploying sequence-based Deep Learning models for NLP tasks
• Familiarity with advanced NLP models like GPT-2, BERT, XLNet
• Background in Computer Vision applications
• Experience with multilingual NLP, including Cyrillic (Russian) and double-byte languages (Japanese, Korean, Chinese) for contract clause recognition
• Proven track record staying ahead of ML industry trends with continuous learning
• Contributions to intelligent document processing or agreement automation systems

Required Skills

• Python programming expertise
• PyTorch or TensorFlow proficiency
• NLP techniques and frameworks (spaCy, scikit-learn)
• Deep Learning model development and deployment
• ML concepts: training, validation, testing, precision/recall, bias/variance
• Data extraction, cleansing, and manipulation at scale
• Sequence-based models (transformers, RNNs)
• Production ML deployment (MLOps)
• C#, Java, or C/C++ development
• Multilingual NLP processing
• Contract/legal document understanding
• Research and evaluation of emerging ML technologies
• Cross-functional collaboration with product teams
• Problem-solving in fast-paced innovation environment
• Continuous learning and adaptability
• Strong communication for technical and non-technical audiences

Benefits

• Competitive salary and equity in a high-growth SaaS leader
• Comprehensive health, dental, and vision insurance
• 401(k) matching and employee stock purchase program
• Unlimited PTO and flexible hybrid work model (minimum 2 days/week in-office)
• Professional development stipend and tuition reimbursement
• Wellness programs including gym memberships and mental health support
• Parental leave and family planning benefits
• Volunteer time off and community impact programs
• DocuSign Cares Fund for employee hardship support

DocuSign is an Equal Opportunity Employer.