Twilio is hiring a
Principal Machine Learning Engineer

Summary

Join the team as our next Principal Machine Learning Engineer on Twilio’s Efficiency Engineering team. As a member of this team, you will have the opportunity to work on groundbreaking projects that directly impact the efficiency and success of our customers.

Requirements

• Proven experience (typically 6+ years) in data science, with a strong emphasis on developing and deploying LLMs and supervised ML models
• Proficiency in programming languages such as Python, or R, and experience with data science frameworks like SciKit-Learn, XGBoost, Keras etc
• Strong understanding of data processing and transformation techniques, including experience with SQL and big data technologies (e.g., Spark, Hadoop)
• Demonstrated ability to develop, train, and deploy ML models and Python apps in production environments
• Experience with cloud platforms (AWS Sagemaker for ML Models) and familiarity with containerization tools like Docker and Kubernetes
• Excellent problem-solving skills, with the ability to translate complex business requirements into actionable AI/ML engineering solutions
• Strong communication skills, capable of articulating complex concepts to both technical and non-technical stakeholders

Responsibilities

• Develop and Deploy Predictive Models: Build and deploy machine learning models, including propensity models and GenAI-powered applications, to production environments, ensuring they meet the diverse needs of Twilio's verticals and customer base
• Collaborate Across Teams: Work closely with product, program, analytics, and engineering teams to implement and refine machine learning, statistical, and forecasting models that drive business outcomes
• Utilize Advanced Technical Stack: Leverage our technical stack, including Python, SQL, R, AWS (Sagemaker, Lambda, S3, Kendra), MySQL, Airtable, and libraries such as Pandas, NumPy, SciKit-Learn, XGBoost, Matplotlib, and Keras, to develop robust and scalable AI/ML solutions
• Integrate Enterprise Data Sources: Effectively utilize enterprise data sources like Salesforce and Zendesk to inform model development and enhance predictive accuracy
• Harness the Power of LLMs: Apply knowledge of Large Language Models (LLMs) such as OpenAI's GPT models, Claude, Gemini, Llama, Whisper, and Groq to develop innovative GenAI use cases and solutions

Benefits

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