Machine Learning Engineer
Greater Dublin (Hybrid) · Full Time
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- Experience
- 5+ yrs
- Salary
- —
- Openings
- 1
- Posted
- 5 ঘন্টা আগে
- Work mode
- Hybrid
- Education
- Bachelor's degree in computer science or related computational field
- Resume
- Required to apply
Job description
About the Company
Docusign facilitates efficient agreement management used globally by over 1.5 million customers and one billion individuals across more than 180 countries. Their Intelligent Agreement Management platform helps businesses streamline contract creation, commitment, and management by integrating critical business data from documents into core business systems.
Role Overview
We are seeking an inventive Machine Learning Engineer passionate about advancing state-of-the-art NLP and ML solutions. This role involves developing prototypes and deploying machine learning models that enhance personalized and automated customer experiences within the Docusign Agreement Cloud. The position reports directly to the Director of Machine Learning and is an individual contributor role.
Responsibilities
- Collaborate with the ML team to research, test, and evaluate current and emerging NLP, ML, and deep learning methods for application on legal or contractual data.
- Apply NLP techniques to maintain and improve existing rule-based, supervised, and unsupervised approaches.
- Utilize ML and deep learning algorithms for NLP tasks such as named entity recognition, part-of-speech tagging, parsing, sentiment analysis, clustering, and text prediction.
- Understand and contribute to the Docusign product’s technological architecture and software development lifecycle, including release management.
- Enhance and support the training, maintenance, and enrichment process for machine learning models.
- Deploy machine learning models to production leveraging current and emerging technologies within the team.
- Collaborate with Product Management to convert product requirements into robust, customer-agnostic machine learning metrics for evaluating success.
Job Location and Work Arrangement
This is a hybrid position requiring a mix of remote work and at least two days per week onsite at a Dublin office, with weekly in-office presence expected. The job designation may be subject to change based on business needs.
Qualifications
- A minimum of 5 years’ experience in designing, developing, deploying, and monitoring machine learning and deep learning solutions.
- Proficient in programming with Python and machine learning frameworks like PyTorch, TensorFlow, spaCy, or scikit-learn.
- Experience in programming languages such as Python, C#, Java, or C/C++.
- Bachelor’s degree in computer science, physics, statistics, econometrics, operations research, applied mathematics, or a related computational field.
Preferred Experience
- Hands-on experience with sequence-based deep learning models.
- Familiarity with leading large language model technologies including GPT, Gemini, and LLaMA.
- Exposure to computer vision applications.
- A strong commitment to continuous learning and staying current with ML industry trends and developments.
- Knowledge of machine learning principles such as training, validation, testing, precision/recall balancing, and bias/variance considerations.
- Expertise in handling and processing large, complex structured and unstructured datasets.
- Contributions to language-agnostic contract clause recognition efforts are advantageous.
Life at Docusign
Docusign fosters an environment that values trust, equality, and inclusiveness, promoting open communication and equal opportunity for all team members. The company prioritizes creating a supportive workplace where employees take pride in their work and their contributions positively impact customers and communities worldwide.
Accommodation and Contact
Reasonable accommodations for qualified individuals with disabilities or religious needs are provided during the application process upon request. For accommodation requests or technical assistance, candidates may reach out via email to the company’s dedicated contact addresses.