Senior NLP/ML Engineer for Contract Analytics

DocuSign, Inc.; United States; Hybrid; USD 130,000 - 170,000; Full time

Benefits offered by this job

Hybrid work arrangement

Office in United States

Flexible schedule

Job summary

DocuSign, Inc. seeks an experienced ML/NLP engineer to build text mining, document understanding, and automation for contractual content on a large-scale platform.

This individual contributor role blends applied NLP research with hands-on model development and deployment, shaping customer experiences while collaborating with product and engineering teams. Hybrid work with in-office presence required.

Qualifications

• 5+ years designing, developing, deploying, and monitoring ML/DL solutions.
• Experience with Python and ML frameworks (PyTorch, TensorFlow, spaCy, scikit-learn).
• Programming experience in Python, C#, Java, or C/C++.
• Bachelor’s degree in a computing/quantitative field.
• Understanding ML concepts: training/validation, precision/recall, bias-variance.
• Experience handling large structured and unstructured data.

Responsibilities

• Investigate NLP, ML, and DL techniques for contractual data.
• Maintain and extend language-processing methods.
• Apply NER, POS tagging, parsing, sentiment analysis, clustering, text prediction.
• Learn product architecture and release processes.
• Support and improve training-data workflows.
• Deploy models to production and measure impact with product teams.

Skills

NLP research

ML deployment

Python

C#

Java

C/C++

PyTorch

TensorFlow

spaCy

scikit-learn

Model deployment

Data wrangling

Education

Bachelor’s degree in computer science or related computational field

Tools

PyTorch

TensorFlow

spaCy

scikit-learn

Job description

DocuSign, Inc. seeks an experienced ML/NLP engineer to build text mining, document understanding, and automation for contractual content on a large-scale platform.

This individual contributor role blends applied NLP research with hands-on model development and deployment, shaping customer experiences while collaborating with product and engineering teams. Hybrid work with in-office presence required.