Company Overview Docusign brings agreements to life.

Over 1.5 million customers and more than a billion people in over 180 countries use Docusign solutions to accelerate the process of doing business and simplify people’s lives.

With intelligent agreement management, Docusign unleashes business-critical data that is trapped inside of documents.

Until now, these were disconnected from business systems of record, costing businesses time, money, and opportunity.

Using Docusign’s Intelligent Agreement Management platform, companies can create, commit, and manage agreements with solutions created by the #1 company in e-signature and contract lifecycle management (CLM).

What you'll do We are seeking an experienced Applied Scientist to join our team.

Our team, originally part of Lexion and recently acquired by Docusign, offers the unique combination of a fast-paced startup environment with the substantial resources and backing of Docusign.

You will be instrumental in leveraging Lexion’s state-of-the-art AI to research, design, and deploy innovative features and products for Docusign’s extensive customer base. A core focus of this role will be the end-to-end design, implementation, and evaluation of machine learning pipelines, working in close partnership with engineering teams to successfully integrate these models into customer-facing functionality.

This position is an individual contributor reporting to a Senior Manager, Machine Learning.

Responsibility Build datasets, sometimes from limited sources, and evaluation for ML systems, working closely with data annotators Design pipelines that include modern ML techniques such as RAG, agent frameworks, language embeddings, and LLMs, and do prompt engineering for ML applications Support product development with rules directly , prompts, and high quality data, overseeing annotation where needed Evaluate the quality of models and product experiences and close the feedback loop for customer-reported AI quality issues Communicate with project stakeholders clearly Identify best practices and improve procedures across data systems Drive and deliver projects from conceptualization through launch and beyond with continual improvement and support Design and conduct product experiments Work in a dynamic, ambiguous work environment Be self-driven and prioritize multiple work streams Collaborate seamlessly with cross-functional teams Operate within an Agile/Scrum framework, supporting engineers in testing their code using managed datasets and evaluation tools Establish and maintain production monitoring for model performance (focusing on AI quality) Job Designation Hybrid: Employee divides their time between in-office and remote work.

Access to an office location is required. (Frequency: Minimum 2 days per week; may vary by team but will be weekly in-office expectation) Positions at Docusign are assigned a job designation of either In Office, Hybrid or Remote and are specific to the role/job.

Preferred job designations are not guaranteed when changing positions within Docusign.

Docusign reserves the right to change a position's job designation depending on business needs and as permitted by local law.

What you bring Basic Masters’s or higher degree in a relevant field (computational linguistics or equivalent field with computational analysis) 6+ years experience in computational linguistics or language data processing

Experience: working in an agile software development environment Proficiency in modern programming languages for ML development, such as Python

Experience: with language annotation and other forms of data markup for ML systems

Experience: with programming and data analysis with languages and platforms such as Python or SQL

Experience: with prompt engineering

Experience: in bootstrapping language data collections in a quickly changing environment

Experience: designing and conducting data experiments.

Experience: with version control, unit tests, and other programming best practices

Experience: with MLOps practices, tools, and platforms (e.g., model versioning, serving, monitoring, and pipeline orchestration)

Experience: with cloud platforms for ML workloads (Azure, AWS, GCP)

Experience: using Git or other modern version control systems Fluency in English both verbal and written Preferred Practical experience with Azure ML, Databricks, or similar cloud-native ML platforms Solid theoretical and practical understanding of common machine learning algorithms, deep learning techniques, and statistical modeling

Experience: collaborating with software engineers to build production-grade software that integrates and leverages advanced Machine Learning technologies (e.g., RAG systems, fine-tuned LLMs, language embeddings) Deep understanding of the relationship between data and machine learning models

Experience: with larger scripting projects that involve combining language data from different sources, computing complex metrics over large datasets, etc Knowledge of basic C# (or willingness to learn)

Experience: working with legal contracts Demonstrated ability to learn and apply new technologies and tool sets with intellectual curiosity

Experience: working for a SaaS company A strong track record as a self-starter who thrives on ownership and individual/team responsibility Commitment to writing high-quality, maintainable, and well-documented code Excellent communication and organizational skills Wage Transparency Pay for this position is based on a number of factors including geographic location and may vary depending on job-related knowledge, skills, and experience.

Based on applicable legislation, the below details pay ranges in the following locations: Washington, Maryland, New Jersey and New York (including NYC metro area): $170,900.00 - $251,325.00 base salary This role is also eligible for the following: Bonus: Sales personnel are eligible for variable incentive pay dependent on their achievement of pre-established sales goals.

Non-Sales roles are eligible for a company bonus plan, which is calculated as a percentage of eligible wages and dependent on company performance.

Stock: This role is eligible to receive Restricted Stock Units (RSUs).

Global benefits provide options for the following: Paid Time Off: earned time off, as well as paid company holidays based on region Paid Parental Leave: take up to six months off with your child after birth, adoption or foster care placement Full Health Benefits Plans: options for 100% employer paid and minimum employee contribution health plans from day one of employment Retirement Plans: select retirement and pension programs with potential for employer contributions Learning and Development: options for coaching, online courses and education reimbursements Compassionate Care Leave: paid time off following the loss of a loved one and other life-changing events Life at Docusign Working here Docusign is committed to building trust and making the world more agreeable for our employees, customers and the communities in which we live and work.

You can count on us to listen, be honest, and try our best to do what’s right, every day.

At Docusign, everything is equal.

We each have a responsibility to ensure every team member has an equal opportunity to succeed, to be heard, to exchange ideas openly, to build lasting relationships, and to do the work of their life.

Best of all, you will be able to feel deep pride in the work you do, because your contribution helps us make the world better than we found it.

And for that, you’ll be loved by us, our customers, and the world in which we live.

Accommodation Docusign is committed to providing reasonable accommodations for qualified individuals with disabilities in our job application procedures.

If you need such an accommodation, or a religious accommodation, during the application process, please contact us at accommodations@docusign.com.

If you experience any issues, concerns, or technical difficulties during the application process please get in touch with our Talent organization at taops@docusign.com for assistance.

Applicant and Candidate Privacy Notice States Not Eligible for Employment This position is not eligible for employment in the following states: Alaska, Hawaii, Maine, Mississippi, North Dakota, South Dakota, Vermont, West Virginia and Wyoming.

Equal Opportunity Employer It's important to us that we build a talented team that is as diverse as our customers and where all employees feel a deep sense of belonging and thrive.

We encourage great talent who bring a range of perspectives to apply for our open positions.

Docusign is an Equal Opportunity Employer and makes hiring decisions based on experience, skill, aptitude and a can-do approach.

We will not discriminate based on race, ethnicity, color, age, sex, religion, national origin, ancestry, pregnancy, sexual orientation, gender identity, gender expression, genetic information, physical or mental disability, registered domestic partner status, caregiver status, marital status, veteran or military status, or any other legally protected category.

EEO Know Your Rights poster:

We are seeking an experienced Applied Scientist to join our team.

Our team, originally part of Lexion and recently acquired by Docusign, offers the unique combination of a fast-paced startup environment with the substantial resources and backing of Docusign.

You will be instrumental in leveraging Lexion’s state-of-the-art AI to research, design, and deploy innovative features and products for Docusign’s extensive customer base. A core focus of this role will be the end-to-end design, implementation, and evaluation of machine learning pipelines, working in close partnership with engineering teams to successfully integrate these models into customer-facing functionality.

This position is an individual contributor reporting to a Senior Manager, Machine Learning.

Responsibility Build datasets, sometimes from limited sources, and evaluation for ML systems, working closely with data annotators Design pipelines that include modern ML techniques such as RAG, agent frameworks, language embeddings, and LLMs, and do prompt engineering for ML applications Support product development with rules directly , prompts, and high quality data, overseeing annotation where needed Evaluate the quality of models and product experiences and close the feedback loop for customer-reported AI quality issues Communicate with project stakeholders clearly Identify best practices and improve procedures across data systems Drive and deliver projects from conceptualization through launch and beyond with continual improvement and support Design and conduct product experiments Work in a dynamic, ambiguous work environment Be self-driven and prioritize multiple work streams Collaborate seamlessly with cross-functional teams Operate within an Agile/Scrum framework, supporting engineers in testing their code using managed datasets and evaluation tools Establish and maintain production monitoring for model performance (focusing on AI quality)
Basic Masters’s or higher degree in a relevant field (computational linguistics or equivalent field with computational analysis) 6+ years experience in computational linguistics or language data processing

Experience: working in an agile software development environment Proficiency in modern programming languages for ML development, such as Python

Experience: with language annotation and other forms of data markup for ML systems

Experience: with programming and data analysis with languages and platforms such as Python or SQL

Experience: with prompt engineering

Experience: in bootstrapping language data collections in a quickly changing environment

Experience: designing and conducting data experiments.

Experience: with version control, unit tests, and other programming best practices

Experience: with MLOps practices, tools, and platforms (e.g., model versioning, serving, monitoring, and pipeline orchestration)

Experience: with cloud platforms for ML workloads (Azure, AWS, GCP)

Experience: using Git or other modern version control systems Fluency in English both verbal and written Preferred Practical experience with Azure ML, Databricks, or similar cloud-native ML platforms Solid theoretical and practical understanding of common machine learning algorithms, deep learning techniques, and statistical modeling

Experience: collaborating with software engineers to build production-grade software that integrates and leverages advanced Machine Learning technologies (e.g., RAG systems, fine-tuned LLMs, language embeddings) Deep understanding of the relationship between data and machine learning models

Experience: with larger scripting projects that involve combining language data from different sources, computing complex metrics over large datasets, etc Knowledge of basic C# (or willingness to learn)

Experience: working with legal contracts Demonstrated ability to learn and apply new technologies and tool sets with intellectual curiosity

Experience: working for a SaaS company A strong track record as a self-starter who thrives on ownership and individual/team responsibility Commitment to writing high-quality, maintainable, and well-documented code Excellent communication and organizational skills