Staff Machine Learning Engineer
New
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TwilioCommunications technology
Remote - USFull-TimeSenior
SalaryBased in Colorado, Hawaii, Illinois, Maryland, Massachusetts, Minnesota, Vermont or Washington D.C. : $188,240 - 235,300. Based in New York, New Jersey, Washington State, or California (outside of the San Francisco Bay area): $199,280 - 249,100. Based in the San Francisco Bay area, California: $221,360 - 276,700.
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Job Details
- Experience
- 4-8 years
- Required Skills
- AWSDockerPythonSQLETLKubernetesMLFlowSnowflakeAirflowSpark
Requirements
- B.S. in Computer Science, Data Engineering, Electrical Engineering, Mathematics, or related field—or equivalent practical experience.
- 4-8 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).
- Experience with 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.
- Experience with 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).
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 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.
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