Machine Learning Engineer

Twilio is hiring a Machine Learning Engineer on the Conversation Intelligence team to build AI-powered capabilities that extract meaning from voice and messaging data at scale.

Responsibilities

• Design and develop machine learning solutions with a focus on accuracy, performance, security, and scalability
• Build and maintain end-to-end AI/ML pipelines, covering data ingestion, feature engineering, model development, validation, and deployment (with guidance from senior engineers on complex architecture decisions)
• Instrument AI/ML services using metrics, logging, and telemetry to track model performance and operational health against defined SLOs
• Join on-call rotations and support progressive rollouts using standard mitigation strategies to keep production inference services healthy
• Collaborate during planning, design, and code review to contribute to product and technical discussions, and improve code quality through detailed 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, including:
  • Proficiency in Python
  • 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, including:
  • Model versioning
  • Experiment tracking
  • Cloud infrastructure experience with AWS, GCP, or Azure
• Proficiency in Python (preferred) or a similar object-oriented language
• Experience using Large (or Small) Language Models within software systems
• Excellent written and verbal communication skills, including the ability to explain complex technical concepts to technical and non-technical audiences

Desired

• 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 practices and tooling for maintaining models in production, including testing, versioning, model registry, retraining, and monitoring

Technologies

• Python
• PyTorch
• TensorFlow
• JAX
• Hugging Face Transformers
• NLTK
• SpaCy
• AWS
• GCP
• Azure

Benefits

• Competitive pay
• Generous time off
• Ample parental and wellness leave
• Healthcare
• Retirement savings program

Location

• Remote (remote) from Ireland

Travel

• Occasional travel may be required for project or team in-person meetings

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