Machine Learning Engineer
Core
Build scalable, low-latency ML-based systems for real-time applications (streaming anomaly detection, recommendation systems, predictive modeling) to serve developers and operators within Twilio's Data & Observability Substrate.
Role type
Senior hands-on IC Machine Learning Engineer
Builds
Production ML systems, data pipelines, and MLOps workflows for real-time customer experiences
Domain
Telecommunications / Data Observability / Real-time ML
Deliverable
production ML models
Required skills
Python, Java, SQL, system design, workflow orchestration, MLOps, containerization, cloud infrastructure, distributed computing, streaming frameworks
Preferred skills
LLMs and generative AI workflows, recommendation systems, time-series modeling, causal inference, open-source contributions
Technologies
Airflow, Kubeflow, SageMaker Feature Store, Snowflake, DynamoDB, OpenSearch, MLflow, LangChain, LangGraph, Galileo, Docker, Kubernetes, Argo CD, Spark, Flink, Kafka Streams
Responsibilities
Translate business problems into measurable ML problem statements; design and maintain enterprise-grade ML solutions; build reproducible ML workflows; implement monitoring and evaluation frameworks; partner cross-functionally to deliver resilient services; own operational excellence (SLAs, on-call, incident response); drive engineering excellence via AI-assisted SDLC and mentoring
Seniority
Senior, hands-on IC
