Sr. Machine Learning Engineer

Company Overview

Docusign brings agreements to life. More than 1.8 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 the Docusign AI-native IAM 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, Open Telemetry, or Jaeger, and Azure and Azure Dev Ops or equivalent

Experience defining model success criteria, measuring detection or risk improvements, communicating ML tradeoffs, and…