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 Machine Learning or Software Engineer, you will contribute to ML model development and build the production software that enables Trust and Safety and Fraud detection. This role combines hands-on machine learning with ownership of backend services, APIs, integrations, and data pipelines that turn model outputs into detection and prevention capabilities. You will work with senior ML and software engineers, Trust and Safety, Fraud, Service Protection, Product, and Data teams to deliver reliable solutions as signals and abuse patterns evolve. This position is an individual contributor role reporting to the Director, Trust and Safety. Responsibility Develop, test, evaluate, and deploy ML models for fraud, spam, abuse, risk scoring, and anomaly detection Build backend services, APIs, integrations, and data pipelines that support ML-driven detection and prevention Integrate models and risk signals into production applications, decisioning systems, and automated interventions Work with senior ML engineers and domain experts to define signals, features, labels, and evaluation approaches Evaluate model performance and tune precision, recall, and false-positive and false-negative tradeoffs Improve existing detection systems through ML or hybrid rules-and-ML approaches Own software components through design, implementation, testing, deployment, observability, and maintenance Partner with Engineering and Data teams on telemetry and infrastructure for high-volume data processing and real-time or near-real-time detection Support model monitoring, retraining, and investigation of performance degradation Participate in design and code reviews, incident response, and operational support, incorporating abuse trends and enforcement outcomes into detection improvements 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 5+ years of related industry experience with a Bachelor's degree or 3 years of related industry experience with a Master's degree Experience building, operating, and maintaining production software and developing, evaluating, or integrating ML models in production environments Experience with C#, Java, or Go, and building backend services, APIs, or distributed applications Experience with machine learning, data processing, feature engineering, and automation using ML frameworks and libraries Experience with supervised learning, classification, anomaly detection, behavioral modeling, and evaluation measures, including precision, recall, class imbalance, false positives, and false negatives Experience building data and feature pipelines using large-scale behavioral, event, or transactional datasets, including streaming or high-volume processing Experience supporting ML deployment and integration into distributed systems, and with reliability, scalability, and production engineering practices Experience with model experimentation, training, validation, monitoring, retraining, and performance degradation Experience with Docker and Kubernetes or similar containerization and orchestration technologies Experience with observability technologies such as Prometheus, Grafana, OpenTelemetry, or Jaeger, and Azure and Azure DevOps or equivalent Experience translating evolving requirements into software and data solutions and collaborating with Engineering, Product, Data, Operations, and domain teams Preferred Experience applying ML to fraud, spam, messaging or account abuse, payment fraud, account takeover, identity risk, cybersecurity, Trust and Safety, or another adversarial domain Experience developing behavioral or account-level risk-scoring models Experience working with noisy or imperfect labels and highly imbalanced datasets Experience integrating model outputs into automated decisioning, enforcement, review, or risk-based interventions Experience incorporating analyst, investigator, or operations feedback and enforcement outcomes into model development Familiarity with hybrid detection approaches combining ML and deterministic rules 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 #LI-Hybrid As a Machine Learning or Software Engineer, you will contribute to ML model development and build the production software that enables Trust and Safety and Fraud detection. This role combines hands-on machine learning with ownership of backend services, APIs, integrations, and data pipelines that turn model outputs into detection and prevention capabilities. You will work with senior ML and software engineers, Trust and Safety, Fraud, Service Protection, Product, and Data teams to deliver reliable solutions as signals and abuse patterns evolve. This position is an individual contributor role reporting to the Director, Trust and Safety. Responsibility Develop, test, evaluate, and deploy ML models for fraud, spam, abuse, risk scoring, and anomaly detection Build backend services, APIs, integrations, and data pipelines that support ML-driven detection and prevention Integrate models and risk signals into production applications, decisioning systems, and automated interventions Work with senior ML engineers and domain experts to define signals, features, labels, and evaluation approaches Evaluate model performance and tune precision, recall, and false-positive and false-negative tradeoffs Improve existing detection systems through ML or hybrid rules-and-ML approaches Own software components through design, implementation, testing, deployment, observability, and maintenance Partner with Engineering and Data teams on telemetry and infrastructure for high-volume data processing and real-time or near-real-time detection Support model monitoring, retraining, and investigation of performance degradation Participate in design and code reviews, incident response, and operational support, incorporating abuse trends and enforcement outcomes into detection improvements Basic 5+ years of related industry experience with a Bachelor's degree or 3 years of related industry experience with a Master's degree Experience building, operating, and maintaining production software and developing, evaluating, or integrating ML models in production environments Experience with C#, Java, or Go, and building backend services, APIs, or distributed applications Experience with machine learning, data processing, feature engineering, and automation using ML frameworks and libraries Experience with supervised learning, classification, anomaly detection, behavioral modeling, and evaluation measures, including precision, recall, class imbalance, false positives, and false negatives Experience building data and feature pipelines using large-scale behavioral, event, or transactional datasets, including streaming or high-volume processing Experience supporting ML deployment and integration into distributed systems, and with reliability, scalability, and production engineering practices Experience with model experimentation, training, validation, monitoring, retraining, and performance degradation Experience with Docker and Kubernetes or similar containerization and orchestration technologies Experience with observability technologies such as Prometheus, Grafana, OpenTelemetry, or Jaeger, and Azure and Azure DevOps or equivalent Experience translating evolving requirements into software and data solutions and collaborating with Engineering, Product, Data, Operations, and domain teams Preferred Experience applying ML to fraud, spam, messaging or account abuse, payment fraud, account takeover, identity risk, cybersecurity, Trust and Safety, or another adversarial domain Experience developing behavioral or account-level risk-scoring models Experience working with noisy or imperfect labels and highly imbalanced datasets Experience integrating model outputs into automated decisioning, enforcement, review, or risk-based interventions Experience incorporating analyst, investigator, or operations feedback and enforcement outcomes into model development Familiarity with hybrid detection approaches combining ML and deterministic rules
