Sr. Machine Learning Engineer

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
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 con