Machine Learning Engineer

Required

• Twilio values diverse experiences from all kinds of industries, and we encourage everyone who meets the required qualifications to apply. If your career is just starting or hasn't followed a traditional path, don't let that stop you from considering Twilio. We are always looking for people who will bring something new to the table!
• Strong foundation in ML/AI (statistics, probability, optimization) with the ability to apply these concepts to real-world problems.
• 5+ years of experience building, deploying, and operating data and ML systems in production.
• Proficient in Python, Java, and SQL; strong software engineering fundamentals (system design, testing, version control, code reviews).
• 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; LLM/agent frameworks such as LangChain/LangGraph; model evaluation/observability tools such as Galileo or similar).
• Working knowledge of containerization and cloud infrastructure, including Docker and Kubernetes, GitOps/CI/CD tools (e.g., Argo CD), and at least one major cloud platform (AWS, GCP, or Azure).
• Understanding of data modeling and scalable systems, including distributed computing and streaming frameworks (e.g., Spark/EMR, Flink, Kafka Streams); familiarity with GPU-based implementation is a plus.
• Demonstrated ability to ramp up quickly and operate effectively in new application/business domains.
• Strong written and verbal communication skills: able to document and present designs and decisions, and comfortable giving/receiving feedback in an Agile environment.
• Familiarity with ML problem areas and techniques, including recommendation systems (e.g., graph-based approaches, two-tower models), time-series modeling (classical and deep learning), representation learning (e.g., embeddings), anomaly detection, and causal inference.
• Practical experience with LLMs and generative AI workflows, including foundation model fine-tuning, RAG, and vector databases.