Machine Learning Engineer
$155,520–$228,700 year
Remote · United States or US
Job Summary
Machine Learning Engineer for Twilio's Data & Observability Substrate, responsible for designing, implementing, and maintaining scalable ML-based systems in production. You'll build end-to-end ML workflows for data preparation, training, evaluation, and inference; implement monitoring and evaluation to improve data quality, model performance, latency, and cost; collaborate with Product, Data Science/ML, Engineering, and Security to deliver resilient services; own operational excellence including SLAs and incident response; drive engineering excellence through AI-assisted SDLC, code reviews, automated testing, and mentoring; and actively adopt AI-assisted practices to improve collaboration and implementation efficiency.
Required Qualifications
- 5+ years of experience building, deploying, and operating data and ML systems in production
- Proficient in Python, Java, and SQL; strong software engineering fundamentals
- Hands-on experience with workflow orchestration and data pipelines (e.g., Airflow, Kubeflow) and cloud data platforms/storage (e.g., SageMaker Feature Store, Snowflake, DynamoDB, OpenSearch)
- Experience with the ML lifecycle and MLOps tooling (e.g., MLflow, Metaflow, SageMaker; LangChain/LangGraph)
- Working knowledge of containerization and cloud infrastructure (Docker, Kubernetes, GitOps/CI/CD tools) and at least one major cloud platform (AWS, GCP, Azure)
- Understanding of data modeling and scalable systems, including distributed computing and streaming frameworks (Spark/EMR, Flink, Kafka Streams)
- Demonstrated ability to ramp up quickly and operate effectively in new application/business domains
- Strong written and verbal communication skills in an Agile environment
- Advanced degree preferred (M.S. or Ph.D.) in a relevant field
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