1 day ago
Base Salary
$179k - $263k/yr
Responsibilities
- Design, develop, evaluate, deploy, and operate production ML models for fraud, spam, abuse, and Trust & Safety risks.
- Build risk and reputation scoring capabilities using behavioral, account, network, device, content, and other signals.
- Translate detection problems into ML requirements, labels, features, evaluation methods, and success criteria.
- Identify emerging abuse patterns and determine when ML, rules, or hybrid approaches are appropriate.
- Develop model outputs that support flagging, throttling, verification, review, blocking, and other enforcement workflows.
- Evaluate detection performance, false-positive and false-negative tradeoffs, and downstream risk impact.
- Establish model monitoring, retraining, versioning, and degradation-detection practices.
- Build data and feature pipelines using large-scale behavioral, event, or transactional datasets, including streaming or high-volume event processing.
- Deploy and serve ML models in distributed systems and integrate them into production applications and decisioning systems.
- Partner with Trust & Safety, Fraud, Engineering, Product, Security, Data, Operations, and domain experts.
- Establish technical direction and 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 handling evolving signals and attacker behavior, class imbalance, noisy labels, model degradation, and false-positive and false-negative tradeoffs.
- Experience with C#, Java, or Go; ML frameworks; data processing; feature engineering; automation; and reproducible experimentation, training, and validation.
- Experience building large-scale data and feature pipelines, including streaming or high-volume event processing.
- Experience deploying and serving ML models in distributed systems while balancing scalability, reliability, latency, throughput, and model performance.
- Experience with MLOps across deployment, monitoring, retraining, versioning, degradation detection, and model health.
- Experience 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.
- Preferred experience includes spam, messaging abuse, account abuse, payment fraud, account takeover, identity risk, reputation scoring, platform integrity, adaptive attackers, analyst investigations, enforcement workflows, hybrid rules-and-ML systems, and scaling ML capabilities in fraud, security, or Trust & Safety organizations.
Benefits
- Hybrid work arrangement requiring a minimum of two days per week in the office, with weekly in-office expectations that may vary by team.
- Company bonus plan eligibility for non-sales employees, based on eligible wages and company performance.
- Restricted Stock Units (RSUs).
- Paid time off and paid company holidays based on region.
- Up to six months of paid parental leave following birth, adoption, or foster care placement.
- Full health benefit plan options, including options that are 100% employer-paid and plans with minimum employee contributions from the first day of employment.
- Retirement and pension programs with potential employer contributions.
- Learning and development options including coaching, online courses, and education reimbursements.
- Paid compassionate care leave.
- Employment is unavailable in Alaska, Hawaii, Maine, Mississippi, North Dakota, South Dakota, Vermont, West Virginia, and Wyoming.
Tech Stack
Categories
About DocuSign
DocuSign builds e-signature, contract lifecycle management, and agreement workflow software used by businesses to prepare, sign, and manage contracts. It sells cloud-based subscriptions and APIs that integrate with systems like Salesforce, Microsoft, and Google to automate document workflows and compliance. Founded in 2003 and headquartered in San Francisco, DocuSign is a public company on NASDAQ with over 1.5 million customers in more than 180 countries.