Machine Learning Engineer

Twilio

2h•Remote

About The Position

Join Twilio’s rapidly-growing AI & Data Platform team as an Machine Learning Engineer. You will design, build, and operate the cloud-native data and ML infrastructure that powers every customer interaction, enabling Twilio’s product teams and customers to move from raw events to real-time intelligence. This is a hands-on, builder-focused role that offers clear technical ownership, mentoring, and growth inside a company defining the future of communications with AI.

Requirements

B.S. in Computer Science, Data Engineering, Electrical Engineering, Mathematics, or related field—or equivalent practical experience.
3–5 years building and operating data or ML systems in production.
Proficient in Python and SQL; comfortable with software engineering fundamentals (testing, version control, code reviews).
Hands-on experience with ETL/ELT orchestration tools (e.g., Airflow, Dagster) and cloud data warehouses (Snowflake, BigQuery, or Redshift).
Familiarity with ML lifecycle tooling such as MLflow, SageMaker, Vertex AI, or similar.
Working knowledge of Docker and Kubernetes and at least one major cloud platform (AWS, GCP, or Azure).
Understanding of data modeling, distributed computing concepts, and streaming frameworks (Spark, Flink, or Kafka Streams).
Strong analytical thinking, communication skills, and a demonstrated sense of ownership, curiosity, and continuous learning.

Nice To Haves

Experience with Twilio Segment, Kafka/Kinesis, or other high-throughput event buses.
Exposure to infrastructure-as-code (Terraform, Pulumi) and GitHub-based CI/CD pipelines.
Practical knowledge of generative AI workflows, foundation-model fine-tuning, or vector databases.
Contributions to open-source data/ML projects or published technical presentations/blogs.
Domain experience in communications, marketing automation, or customer engagement analytics.

Responsibilities

Architect, implement, and maintain scalable data pipelines and feature stores for batch and real-time workloads.
Build reproducible ML training, evaluation, and inference workflows using modern orchestration and MLOps tooling.
Integrate event streams from Twilio products (e.g., Messaging, Voice, Segment) into unified, analytics-ready datasets.
Monitor, test, and improve data quality, model performance, latency, and cost.
Partner with product, data science, and security teams to ship resilient, compliant services.
Automate deployment with CI/CD, infrastructure-as-code, and container orchestration best practices.
Produce clear documentation, dashboards, and runbooks; share knowledge through code reviews and brown-bag sessions.
Embrace Twilio’s “We are Builders” values by taking ownership of problems and driving them to completion.