Machine Learning Engineer

About the Role

The role is situated within Twilio's remote-first culture, which emphasizes global inclusion, diversity, and connection among team members. You will have the opportunity to work alongside experienced ML practitioners, contributing to the development of real features that directly impact how businesses understand their customers. The team's focus on AI, machine learning, and data analysis makes this an exciting opportunity for professionals looking to grow in a field with exponential demand.

What You Will Do

• Design and develop machine learning solutions that ensure accuracy, performance, security, and scalability.
• Implement and maintain end-to-end AI/ML pipelines, from data ingestion and feature engineering to model development, validation, and deployment.
• 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 and helping raise overall code quality through thoughtful review feedback.
• Develop and deploy solutions that extract meaning from voice and messaging data at Twilio scale.
• Work alongside experienced ML practitioners to ship real features, from model pipelines to production inference, that directly shape how businesses understand their customers.
• Ensure the security, integrity, and compliance of AI/ML solutions with Twilio's standards and regulatory requirements.
• Stay updated with the latest advancements in machine learning and AI, applying this knowledge to continuously improve Twilio's offerings.

What We Are Looking For

• 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).
• Experience developing, testing, and deploying small-to-medium scoped ML services or features in a collaborative engineering environment.
• Proficiency in Python (preferred) or similar OO language.
• Familiarity with NLP libraries such as Hugging Face Transformers, NLTK, or SpaCy.
• Experience with cloud-based infrastructure (AWS, GCP, or Azure) and model versioning, experiment tracking.
• Strong understanding of machine learning principles, including supervised and unsupervised learning, deep learning, and model evaluation metrics.

Nice to Have

• Experience with Excel for data analysis and visualization.
• Knowledge of containerization using Docker and Kubernetes for deployment and scaling of ML models.
• Familiarity with agile development methodologies and version control systems like Git.
• Participation in open-source projects or personal projects that demonstrate machine learning skills.

Benefits and Perks

• Competitive salary and benefits package, tailored to attract top talent in the field.
• Opportunities for professional growth and development in a rapidly expanding company.
• Remote work environment with flexible hours, allowing for a healthy work-life balance.
• Access to cutting-edge technologies and innovative projects that are shaping the future of communications.
• Collaborative and inclusive team culture that values diversity and global perspectives.
• Comprehensive health insurance and wellness programs to support your physical and mental health.
• Generous PTO policy, including vacation days, sick leave, and holidays, to ensure you have time to relax and recharge.

• Tip: Showcase your proficiency in Python and machine learning frameworks through personal projects or open-source contributions.
• Ensure your resume and cover letter highlight experience with cloud-based infrastructure and NLP libraries.
• Prepare to discuss your approach to model development, deployment, and maintenance during the interview process.
• Demonstrate your understanding of machine learning principles and how they apply to real-world problems.
• Be ready to talk about your experience with data analysis, including any experience with Excel for data visualization.
• Consider reaching out to current or former Twilio employees for insights into the company culture and what makes a successful candidate.
• Highlight any experience with containerization, agile methodologies, and version control systems as these are valuable assets in a machine learning engineering role.