20 hours ago
São Paulo, BrazilMid Level / Senior
Responsibilities
- Develop, test, evaluate, deploy, monitor, and retrain machine-learning models for fraud, spam, abuse, risk scoring, and anomaly detection.
- Build and maintain backend services, APIs, integrations, and data and feature pipelines for ML-driven detection and prevention.
- Integrate model outputs and risk signals into production applications, decisioning systems, and automated interventions.
- Define signals, features, labels, and evaluation approaches with ML engineers and domain experts.
- Evaluate model performance and tune precision, recall, false-positive, and false-negative tradeoffs.
- Improve detection systems using machine learning 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 and real-time or near-real-time processing.
- Participate in design reviews, code reviews, incident response, operational support, and investigations of performance degradation.
Requirements
- 5+ years of related industry experience with a bachelor’s degree, or 3+ years with a master’s degree.
- Experience building, operating, and maintaining production software and developing, evaluating, or integrating ML models in production.
- Experience with C#, Java, or Go and backend services, APIs, or distributed applications.
- Experience with machine learning, data processing, feature engineering, automation, supervised learning, classification, anomaly detection, behavioral modeling, and ML evaluation measures.
- Experience building data and feature pipelines from large-scale behavioral, event, or transactional datasets, including streaming or high-volume processing.
- Experience deploying and integrating ML systems into distributed environments and applying reliability, scalability, and production engineering practices.
- Experience with model experimentation, training, validation, monitoring, retraining, and performance degradation analysis.
- Experience with Docker, Kubernetes or similar containerization and orchestration technologies, observability tools, and Azure and Azure DevOps or equivalents.
- Experience translating evolving requirements into software and data solutions while collaborating across Engineering, Product, Data, Operations, and domain teams.
- Preferred experience includes fraud, spam, account abuse, payment fraud, account takeover, identity risk, cybersecurity, Trust and Safety, behavioral risk scoring, imbalanced datasets, automated decisioning, analyst feedback, and hybrid ML and deterministic-rule approaches.
Benefits
- Hybrid work arrangement requiring at least two days per week in the office, with frequency potentially varying by team.
- Docusign provides reasonable accommodations for qualified individuals with disabilities and for religious accommodations during the application process.
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.