machine learning engineer for conversational AI

Twilio delivers communications solutions to hundreds of thousands of businesses and empowers millions of developers to create personalized customer experiences.

• 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

• 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
• Будет плюсом: 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

• 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