Sr. Machine Learning Engineer

Help advance DocuSign’s machine learning capabilities to detect and prevent abuse and fraud across 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 using behavioral, account, network, device, content, and other relevant signals
• Translate detection challenges into ML requirements, including labels, features, evaluation methods, and success criteria
• Spot emerging abuse patterns and determine where ML, rules, or hybrid approaches improve detection
• Create model outputs that enable flagging, throttling, verification, review, or blocking
• Assess detection performance, false-positive and false-negative tradeoffs, and downstream risk impact
• Define model monitoring, retraining, and degradation detection practices
• Partner with Trust & Safety and Fraud experts on adversary behavior and enforcement impact
• Collaborate with Engineering, Data, Product, and Security to integrate data and 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/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 handling detection problems with incomplete or evolving signals, labels, requirements, or abuse patterns, including changing attacker behavior, class imbalance, noisy labels, degradation, and FP/FN 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 scalability, reliability, latency, throughput, and model performance
• Experience with MLOps across the model lifecycle: deployment, monitoring, retraining, versioning, degradation detection, and model health, including ML workloads using Docker and Kubernetes
• Experience with observability tools such as Prometheus, Grafana, OpenTelemetry, or Jaeger, plus 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 and/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

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

Benefits

• Bonus: non-Sales roles eligible for a company bonus plan calculated as a percentage of eligible wages and dependent on company performance; Sales personnel eligible for variable incentive pay tied to pre-established sales goals
• Stock: eligible for 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 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
• 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

Work Location and Designation

• Seattle, WA (hybrid)
• Hybrid: time split between in-office and remote; office access required
• In-office frequency: minimum 2 days per week; weekly in-office expectation may vary by team
• Job designations (In Office, Hybrid, Remote) are specific to the role and not guaranteed when changing positions; DocuSign may change designation based on business needs and local law

Job Designation

• Hybrid

Minimum Experience and Education

• Minimum experience: 8 years
• Education: Bachelor’s degree (or equivalent experience)

Work Authorization

• DocuSign does not provide visa sponsorship or immigration support for this position
• Applicants must be authorized to work in the United States on a full-time, permanent basis without current or future sponsorship