About the Job
At Twilio, we’re shaping the future of communications, all from the comfort of our homes. We deliver innovative solutions to hundreds of thousands of businesses and empower millions of developers worldwide to craft personalized customer experiences.
Our dedication to remote-first work, and strong culture of connection and global inclusion means that no matter your location, you’re part of a vibrant team with diverse experiences making a global impact each day. As we continue to revolutionize how the world interacts, we’re acquiring new skills and experiences th...
We use Artificial Intelligence (AI) to help make our hiring process efficient. That said, every hiring decision is made by real Twilions!
Key Responsibilities
In this role, you’ll:
Design and development of machine learning solutions, ensuring accuracy, performance, security, and scalability.
Implement and maintain end-to-end AI/ML pipelines - from data ingestion and feature engineering through to model development, validation, and deployment with guidance from senior engineers on complex architectural decisions
Instrument AI/ML services with appropriate metrics, logging, and telemetry to monitor model performance and operational health against defined SLOs
Participate in on-call rotations, executing progressive rollouts and applying standard mitigation strategies to keep production inference services healthy
Collaborate across planning, design, and code review phases contributing to product and technical discussions, while helping raise overall code quality through thoughtful review feedback
Required Skills & Abilities
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!
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, with demonstrated proficiency in Python and at least one ML framework (PyTorch, TensorFlow, or JAX) and 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, and cloud-based infrastructure (AWS, GCP, or Azure)
Proficiency in Python (preferred) or similar OO language.
Experience utilizing Large (or Small) Language Models within software systems.
Excellent written and verbal communication skills with the ability to articulate complex technical concepts to both technical and non-technical audiences.
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, CrewAI
Familiarity with MLOps/LLMOps tooling related to maintaining models in production such as testing, versioning, model registry, retraining, and monitoring.
Qualifications
Experience:
2 years experience
Education:
Bachelor
