ML Engineer
Founding ML Engineer building production ML systems for governance, security, and agentic platform capabilities at Docker. Requires 5+ years applied ML experience shipping systems and 4+ years backend/infra engineering.
Machine Learning Engineer
Design, build, and operate cloud-native data and ML infrastructure powering real-time intelligence for Twilio products. Requires 3-5 years of production ML/data systems experience and strong Python/SQL skills.
139k – 204kUnited StatesML EngineeringRemote3+ YOE
About the role
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.
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.
Compensation & Benefits
- Health care insurance, 401(k) retirement account, paid sick time, paid personal time off, paid parental leave.
- This role may be eligible to participate in Twilio’s equity plan and corporate bonus plan.
Skills
PythonSQLAirflowDagsterSnowflakeBigQueryRedshiftMLflowSageMakerVertex AiDockerKubernetesAWSGCPAzure
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