Sr. Machine Learning Engineer

Company Overview Docusign brings agreements to life. More than 1.8 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 the Docusign AI-native IAM 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 As a Senior Machine Learning Engineer, you will advance machine learning capabilities that help detect and prevent abuse and fraud across Docusign products and services. Your work will support earlier identification of harmful behavior and risk-based interventions. You will partner with Trust & Safety, Fraud, Engineering, Product, Security, and Data teams to improve detection as abuse patterns evolve. This position is an individual contributor role reporting to the Director of Trust & Safety. Responsibility Design, develop, evaluate, and deploy production ML models for fraud, spam, abuse, and other Trust & Safety risks Build risk and reputation scoring capabilities using behavioral, account, network, device, content, and other relevant signals Translate detection problems into ML requirements, including labels, features, evaluation methods, and success criteria Identify emerging abuse patterns and determine where ML, rules, or combined approaches improve detection Develop model outputs that support flagging, throttling, verification, review, or blocking Evaluate detection performance, false-positive and false-negative tradeoffs, and downstream risk impact Establish model monitoring, retraining, and degradation detection practices Partner with Trust & Safety and Fraud experts on adversary behavior and enforcement impact, and with Engineering, Data, Product, and Security on data and production integration Establish technical direction and foundational ML practices for Trust & Safety and Fraud 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 Bachelor’s degree or equivalent experience and 8+ years of related industry experience Experience developing, deploying, operating, and measuring production machine learning models, including supervised and unsupervised learning, classification, risk prediction, anomaly or behavioral detection, and model evaluation Experience applying ML to fraud detection, spam or abuse detection, cybersecurity, Trust & Safety, anti-abuse, risk, or another adversarial domain Experience solving detection problems with incomplete or evolving signals, labels, requirements, or abuse patterns, including changing attacker behavior, class imbalance, noisy labels, model degradation, and false-positive and false-negative tradeoffs Experience with C#, Java, or Go, and ML frameworks, data processing, feature engineering, automation, and reproducible experimentation, training, and validation Experience building data and feature pipelines using large-scale behavioral, event, or transactional datasets, including streaming or high-volume event processing Experience deploying and serving ML models in distributed systems, integrating outputs into production applications or decisioning systems, and balancing system design, scalability, reliability, latency, throughput, and model performance Experience with MLOps across the model lifecycle, including deployment, monitoring, retraining, versioning, degradation detection, and model health, and deploying ML workloads using Docker and Kubernetes Experience with observability technologies such as Prometheus, Grafana, OpenTelemetry, or Jaeger, and Azure and Azure DevOps or equivalent Experience defining model success criteria, measuring detection or risk improvements, communicating ML tradeoffs, and collaborating with Product, Engineering, Data, Operations, and domain experts Preferred Experience building ML systems for spam detection, messaging abuse, account abuse, payment fraud, account takeover, identity risk, reputation scoring, or platform integrity Experience developing account, entity, or behavioral risk-scoring models that combine multiple signals Experience detecting attackers who adapt to controls and integrating ML outputs into automated prevention, enforcement, or risk-based decisioning workflows Experience incorporating analyst decisions, investigations, or enforcement outcomes into model development Experience improving heuristic detection systems through ML or hybrid rules-and-ML approaches Experience establishing or scaling ML capabilities within a fraud, security, or Trust & Safety organization 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): $178,900.00 - $262,825.00 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 Work Authorization Notice: Please note that we do not provide visa sponsorship or immigration support for this position. Applicants must already be authorized to work in the United States on a full-time, permanent basis without the need for current or future sponsorship. 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 #LI-Hybrid

As a Senior Machine Learning Engineer, you will advance machine learning capabilities that help detect and prevent abuse and fraud across Docusign products and services. Your work will support earlier identification of harmful behavior and risk-based interventions. You will partner with Trust & Safety, Fraud, Engineering, Product, Security, and Data teams to improve detection as abuse patterns evolve. This position is an individual contributor role reporting to the Director of Trust & Safety. Responsibility Design, develop, evaluate, and deploy production ML models for fraud, spam, abuse, and other Trust & Safety risks Build risk and reputation scoring capabilities using behavioral, account, network, device, content, and other relevant signals Translate detection problems into ML requirements, including labels, features, evaluation methods, and success criteria Identify emerging abuse patterns and determine where ML, rules, or combined approaches improve detection Develop model outputs that support flagging, throttling, verification, review, or blocking Evaluate detection performance, false-positive and false-negative tradeoffs, and downstream risk impact Establish model monitoring, retraining, and degradation detection practices Partner with Trust & Safety and Fraud experts on adversary behavior and enforcement impact, and with Engineering, Data, Product, and Security on data and production integration Establish technical direction and foundational ML practices for Trust & Safety and Fraud

Basic Bachelor’s degree or equivalent experience and 8+ years of related industry experience Experience developing, deploying, operating, and measuring production machine learning models, including supervised and unsupervised learning, classification, risk prediction, anomaly or behavioral detection, and model evaluation Experience applying ML to fraud detection, spam or abuse detection, cybersecurity, Trust & Safety, anti-abuse, risk, or another adversarial domain Experience solving detection problems with incomplete or evolving signals, labels, requirements, or abuse patterns, including changing attacker behavior, class imbalance, noisy labels, model degradation, and false-positive and false-negative tradeoffs Experience with C#, Java, or Go, and ML frameworks, data processing, feature engineering, automation, and reproducible experimentation, training, and validation Experience building data and feature pipelines using large-scale behavioral, event, or transactional datasets, including streaming or high-volume event processing Experience deploying and serving ML models in distributed systems, integrating outputs into production applications or decisioning systems, and balancing system design, scalability, reliability, latency, throughput, and model performance Experience with MLOps across the model lifecycle, including deployment, monitoring, retraining, versioning, degradation detection, and model health, and deploying ML workloads using Docker and Kubernetes Experience with observability technologies such as Prometheus, Grafana, OpenTelemetry, or Jaeger, and Azure and Azure DevOps or equivalent Experience defining model success criteria, measuring detection or risk improvements, communicating ML tradeoffs, and collaborating with Product, Engineering, Data, Operations, and domain experts Preferred Experience building ML systems for spam detection, messaging abuse, account abuse, payment fraud, account takeover, identity risk, reputation scoring, or platform integrity Experience developing account, entity, or behavioral risk-scoring models that combine multiple signals Experience detecting attackers who adapt to controls and integrating ML outputs into automated prevention, enforcement, or risk-based decisioning workflows Experience incorporating analyst decisions, investigations, or enforcement outcomes into model development Experience improving heuristic detection systems through ML or hybrid rules-and-ML approaches Experience establishing or scaling ML capabilities within a fraud, security, or Trust & Safety organization