Sr. Machine Learning Engineer

DocuSign is hiring a Sr. Machine Learning Engineer to advance machine learning capabilities that detect and prevent abuse and fraud across DocuSign products and services.

Responsibilities

• Design, develop, evaluate, and deploy production machine learning 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 challenges 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 enable flagging, throttling, verification, review, or blocking
• Evaluate detection performance, including 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
• Partner with Engineering, Data, Product, and Security to integrate data and model outputs into production systems
• Set technical direction and establish foundational ML practices for Trust & Safety and Fraud

Requirements

• Bachelor’s degree or equivalent experience and 8+ years of related industry experience
• Experience developing, deploying, operating, and measuring production ML 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/false-negative tradeoffs
• Experience with C#, Java, or Go, plus 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 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
• Experience deploying ML workloads using Docker and Kubernetes
• Experience with observability tools 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
• 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 using ML or hybrid rules-and-ML approaches
• Experience establishing or scaling ML capabilities within a fraud, security, or Trust & Safety organization

Technologies

• C#, Java, Go
• Docker, Kubernetes
• Prometheus, Grafana
• OpenTelemetry, Jaeger
• Azure, Azure DevOps

Compensation

• USD 178,900 - 262,825 per year
• Pay varies based on geographic location and job-related knowledge, skills, and experience
• Washington, Maryland, New Jersey and New York (including NYC metro area): $178,900.00 - $262,825.00 base salary

Benefits

• Bonus: Sales personnel may be eligible for variable incentive pay based on pre-established sales goals; non-Sales roles may be eligible for a company bonus plan calculated as a percentage of eligible wages and dependent on company performance
• Stock: eligible to receive Restricted Stock Units (RSUs)
• Paid Time Off: earned time off and paid company holidays based on region
• Paid Parental Leave: up to six months off after birth, adoption, or foster care placement
• Full Health Benefits Plans: options for 100% employer paid and minimum employee contribution plans from day one of employment
• Retirement Plans: select retirement and pension programs with potential for employer contributions
• Learning and Development: coaching, online courses, and education reimbursements
• Compassionate Care Leave: paid time off following the loss of a loved one and other life-changing events

Job Designation & Location

• Location: Seattle, WA (hybrid)
• Hybrid: employee divides time between in-office and remote work; access to an office location is required
• Minimum in-office expectation: at least 2 days per week; may vary by team but weekly in-office expectation
• Job designations may change based on business needs and local law

Work Authorization Notice

• DocuSign does 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 current or future sponsorship