About
Twilio delivers communications solutions to hundreds of thousands of businesses and empowers millions of developers to create personalized customer experiences.
Responsibilities
•Design and develop machine learning solutions that meet accuracy, performance, security, and scalability requirements
•Implement and maintain end-to-end AI/ML pipelines, from data ingestion and feature engineering through model development, validation, and deployment, with guidance from senior engineers on complex architectural decisions
•Instrument AI/ML services with metrics, logging, and telemetry to monitor model performance and operational health against defined SLOs
•Participate in on-call rotations, execute progressive rollouts, and apply standard mitigation strategies to maintain healthy production inference services
•Collaborate in planning, design, and code reviews; contribute to product and technical discussions and help improve code quality through thoughtful review feedback
Requirements
•Bachelor's degree in Computer Science, Mathematics, Statistics, or a related quantitative field, or equivalent practical experience
•2+ years of experience in machine learning engineering or applied ML
•Proficiency in Python and at least one ML framework: PyTorch, TensorFlow, or JAX
•Familiarity with NLP libraries such as Hugging Face Transformers, NLTK, or SpaCy
•Experience developing, testing, and deploying small-to-medium scoped ML services or features in a collaborative engineering environment
•Experience with model versioning, experiment tracking, and cloud-based infrastructure such as AWS, GCP, or Azure
•Proficiency in Python (preferred) or a similar OO language
•Experience using Large or Small Language Models within software systems
•Excellent written and verbal communication skills, with the ability to explain complex technical concepts to technical and non-technical audiences
Nice to have
•Hands-on experience with conversational AI or LLM fine-tuning and prompt engineering in a production context
•Exposure to agentic AI frameworks such as LangGraph, AutoGen, or CrewAI
•Familiarity with MLOps/LLMOps tools for maintaining production models, including testing, versioning, model registry, retraining, and monitoring
Conditions
•Competitive pay, generous time off, parental and wellness leave, healthcare, and a retirement savings program; offerings vary by location
•Occasional travel may be required for in-person project or team meetings
