Staff Machine Learning Engineer (L4)

About this job

As a Staff Machine Learning Engineer (L4) at Twilio on the Conversation Memory team in India, your normal day involves building foundational AI and machine learning capabilities that power intelligent customer engagement experiences. You will operate in a remote setting to design and execute rapid experiments, bridge theoretical research with enterprise-quality software, and create scalable ML systems. The team focuses on contextual intelligence and personalization, ensuring that human or AI agents understand customer history and preferences seamlessly. Your day-to-day work includes leading research cycles into LLM orchestration and retrieval strategies, building functionality for products seeing rapid adoption, and utilizing the latest AI stacks to deliver high-scale solutions.

Responsibilities

• Build out new functionality within a fast-moving product seeing rapid adoption.
• Take a nebulous idea, design a rigorous experiment, and execute it within weeks.
• Conduct deep-dive research into state-of-the-art LLM orchestration, retrieval strategies, and data-driven personalization.
• Build ML systems that hold up at Twilio scale.
• Rapidly acquire new technical skills and knowledge in a fast-paced, high-delivery environment.
• Proactively leverage and integrate the latest AI development stacks to focus on core differentiation.

Requirements

• 8+ years applied ML/AI experience with a track record of owning technically ambitious systems end to end.
• Comfort operating in a 0-to-1 setting with ambiguous requirements and a strong bias for action.
• Proficiency in Python programming language.
• Experience building cloud-based services on AWS, GCP, or Azure with high-volume data and real-time inference paths.
• Strong fundamentals of statistical machine learning algorithms, transformer models, and LLMs.
• Ability to raise the technical bar of a team through mentorship, design review, and standards.
• Strong communication skills and highly effective collaboration abilities.

Good to have

• Prior experience building autonomous agents that handle multi-turn conversations and maintain long-term context.
• Strong background in predictive modeling on user behavior, including experiment design and causal or counterfactual methods.
• Prior experience working in a globally distributed team.

Who should apply

This role suits experienced machine learning professionals with a strong background in applied AI and end-to-end system ownership. Candidates should thrive in ambiguous 0-to-1 settings, possess robust Python skills, and have extensive experience with cloud-based services and large language models. The posting does not mention freshers and explicitly requires 8+ years of applied ML/AI experience.

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