About the job
The Traffic Intelligence organization at Twilio establishes trusted communications, ensuring that every interaction is safe, wanted and legal. This position is needed to build advanced machine learning models and robust data pipelines that enable fast, accurate risk predictions and decisions at scale across all communication channels including messaging, voice, email. The engineering team leads systems that are highly scalable and determines if the communications are legal; and wanted in near real time or prior to the communications being sent from the platform. This role requires a blend of technical depth in distributed systems and a strategic mindset to partner with Product and Engineering leaders to execute an AI/ML roadmap.This role interfaces with several key stakeholders and works on initiatives that are tracked at the executive level.
Responsibilities
- Manage and Mentor a team of talented Machine Learning and Data Engineers with various levels of experience and positively influencing their careers.
- Partner with Product Managers and Architects to distill customer needs into actionable technical requirements and long-term roadmaps.
- Ensure best engineering practices & oversee end-to-end execution of large-scale ML solutions with operational excellence.
- Work with data platform teams to build robust, scalable batch and real-time data pipelines.
- Work closely with the Fraud and Compliance Operations team and the Data Analytics team to identify and understand the ever-changing landscape of fraud vectors and then take action to keep up with new forms of fraud.
- Work with other product and development teams to ensure that Twilio’s new and existing products incorporate anti-fraud efforts and publish the appropriate data to our fraud-fighting tools.
- Advocate for agile processes, continuous integration (CI/CD), automated testing, and sophisticated model monitoring to minimize "toil."
- Institute and maintain a rotating on-call incident escalation and response processes for the team.
- Manage highly critical risk platform tools in the cloud.
- Own reliability for the team’s services and participate in an on-call rotation.
- Adapt to prioritizing multiple issues in a high-pressure environment.
- Understand complex architectures and be comfortable working with multiple teams.
- Conduct short term and long term planning to achieve team’s goals identifying both tactical and strategic commitments and gaps while performing capacity management and ruthless prioritization.
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
- 10–14+ years of total experience in machine learning /data engineering.
- 5+ years of experience leading and managing engineering teams.
- Proven track record of shipping and maintaining ML models in a fast-paced, production environment.
- Experience developing highly-available full stack applications and distributed systems
- Stellar communication, organization and management skills with proven track record in an agile environment.
- Ability to explain your technical and business decisions succinctly as well as in detail.
- Languages: Expert proficiency in Python. Familiarity with Java or Scala is a plus.
- ML Frameworks: Deep experience with PyTorch, TensorFlow, or Keras.
- Data Tools: Experience with Kafka, Apache Spark, Hadoop, Presto, and DynamoDB.
- Cloud Platforms: Significant experience with AWS (specifically SageMaker, EKS, or ECS).
- Observability: Familiarity with tools like Datadog and Grafana.
Desired Qualifications
- Experience working in fraud/compliance domain.
- Knowledge of telecommunications.
- Familiarity with popular LLMs and latest developments.