New opportunity

Principal, Machine Learning Engineer (L5)

Twilio · India

About this role

Twilio is seeking a Principal Machine Learning Engineer (L5) to join the Customer Memory Team. This role focuses on building foundational AI/ML capabilities that power intelligent engagement experiences, enabling applications and AI agents to maintain customer context. You will bridge the gap between theoretical research and production-grade software, leading rapid research cycles to solve complex problems in contextual intelligence and large-scale machine learning. Responsibilities: Build new product functionality; design and execute rigorous experiments; conduct deep-dive research into LLM orchestration, retrieval strategies, and personalization; build ML systems at scale; and integrate modern AI development stacks. Qualifications: Required: 10+ years of applied ML/AI experience with a track record of end-to-end system ownership; proficiency in Python; experience with cloud-based services (AWS, GCP, or Azure) including high-volume data and real-time inference; deep expertise in production-grade ML/AI systems, LLM orchestration, embedding models, and vector stores; strong fundamentals in statistical ML and transformer models; and the ability to mentor and raise technical standards. Desired: Experience building autonomous agents for multi-turn conversations, background in predictive modeling on user behavior, and experience working in globally distributed teams. Benefits: Competitive pay, generous time off, parental and wellness leave, healthcare, and retirement savings programs. The role is remote-first in India, with occasional travel required for team meetings.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

PythonMachine LearningLLM orchestrationEmbedding modelsVector storesTransformer modelsStatistical machine learningCloud computingAWSGCPAzureSystem architecturePredictive modelingExperiment designAutonomous agents

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

End-to-End System Ownership
The role requires a proven track record of owning technically ambitious ML systems from conception to production. You must demonstrate the ability to drive architectural decisions and implementation independently, moving beyond contributing to existing designs.
Production-Grade LLM Orchestration
You must possess deep expertise in deploying LLMs, specifically focusing on retrieval strategies, embedding models, and vector stores. The priority is bridging the gap between research-level experimentation and scalable, reliable production systems.
Technical Leadership and Mentorship
As a Principal-level engineer, you are expected to raise the technical bar of the team. This involves conducting rigorous design reviews, establishing engineering standards, and mentoring peers without relying on formal management authority.
Rapid Experimentation in Ambiguous Environments
You must be comfortable operating in a 0-to-1 setting where requirements are nebulous. The ability to design, execute, and iterate on experiments within weeks is critical to the team's fast-moving product development cycle.

Likely challenges to think through

Balancing Research with Production Constraints
The core difficulty lies in maintaining high-quality research cycles while ensuring the resulting systems meet enterprise-grade reliability and scale. Prepare by discussing how you manage technical debt and performance trade-offs when integrating experimental AI features into production pipelines.
Navigating Ambiguity in 0-to-1 Projects
Operating without clear requirements can lead to scope creep or misalignment. Prepare by sharing examples of how you have defined success metrics and technical roadmaps when starting from a blank slate.
Influencing Without Authority
Raising technical standards across a team requires strong soft skills and the ability to build consensus. Prepare by reflecting on how you have successfully advocated for architectural changes or best practices when faced with resistance or competing priorities.

Practice your answers

Technical practice

Designing Production-Grade LLM Orchestration

What to coverDiscuss the trade-offs between different LLM orchestration frameworks and how you ensure low-latency, reliable inference in a high-volume production environment. Focus on error handling, cost management, and maintaining context across multi-turn interactions.

Vector Store Selection and Optimization

What to coverExplain your criteria for selecting a vector database for large-scale retrieval tasks. Address how you handle index updates, latency requirements for real-time retrieval, and the impact of different embedding models on search accuracy.

Scaling Real-Time ML Inference

What to coverDescribe your approach to building and scaling real-time inference services on cloud infrastructure. Discuss strategies for managing high-volume data streams, model versioning, and ensuring system resilience during traffic spikes.

Experiment Design for User Behavior Modeling

What to coverExplain how you design rigorous experiments when randomization is partial or difficult to achieve. Discuss the use of causal or counterfactual methods to validate predictive models in complex user-engagement scenarios.

Transformer Model Optimization

What to coverDetail your experience in deploying and optimizing transformer models for production. Focus on techniques for model compression, quantization, or distillation to balance performance with inference speed.

Building Autonomous Agents

What to coverDiscuss the architectural challenges of building autonomous agents that maintain long-term context and consistency. Address how you manage state, handle memory retrieval, and ensure the agent remains aligned with user intent over extended conversations.

System Architecture for Contextual Intelligence

What to coverDescribe how you would design a system to maintain customer context across multiple channels and applications. Focus on data consistency, privacy considerations, and the integration of diverse data sources into a unified memory layer.

Bridging Research and Production

What to coverProvide an example of how you have transitioned a theoretical ML concept into a production-grade feature. Highlight the specific trade-offs you made between model complexity and engineering constraints.

Behavioural practice

Operating in a 0-to-1 Ambiguous Environment

What to coverDescribe a situation where you had to define a project from scratch with minimal requirements. Explain how you identified the core problem, gained stakeholder alignment, and executed the initial phase of the project.

Raising Technical Standards Without Formal Authority

What to coverGive an example of how you influenced a team's technical direction or improved engineering standards without being in a formal management role. Focus on how you communicated your rationale and gained buy-in from peers.

Collaborating in Globally Distributed Teams

What to coverShare an experience working within a globally distributed team. Explain the communication strategies you used to ensure alignment, maintain momentum, and overcome challenges related to time zones or cultural differences.

Handling Technical Debt vs. Rapid Delivery

What to coverDescribe a time you had to balance the need for rapid product delivery with the long-term health of the system. How did you decide what to prioritize and how did you communicate these trade-offs to non-technical stakeholders?

Sharpen your knowledge

Topics to prepare

LLM Orchestration and Retrieval Strategies
The role requires deep expertise in designing systems that manage LLM workflows and retrieval-augmented generation (RAG). Exercise: Review architectural patterns for multi-step LLM chains and evaluate trade-offs between different retrieval strategies (e.g., semantic search vs. hybrid search) to ensure context consistency in conversational agents.
Production-Grade ML System Design
Focus on building systems that handle high-volume data and real-time inference at scale. Exercise: Draft a system design document for a real-time personalization engine, focusing on latency, data consistency, and the integration of vector stores within a cloud-native architecture.
Causal Inference and Experiment Design
The role involves rigorous experimentation, particularly when randomization is partial. Exercise: Study causal inference methods for evaluating user behavior changes in production environments where A/B testing is not fully feasible.

Skills to revise

Vector Store Integration
Practical implementation of vector databases is essential for contextual intelligence. Exercise: Build a prototype using a vector store to manage long-term memory for an AI agent, ensuring efficient retrieval of customer context during multi-turn conversations.
Python for High-Volume Inference
Proficiency in Python is required for building production-grade ML services. Exercise: Optimize a Python-based inference pipeline for low-latency performance, focusing on efficient data handling and asynchronous processing for real-time applications.
Cloud-Based ML Deployment
Experience with AWS, GCP, or Azure is required for deploying scalable services. Exercise: Design a deployment strategy for an ML model on a cloud provider, incorporating monitoring, logging, and automated scaling for high-volume traffic.

Your preparation checklist

Prepare end-to-end system ownership case studies
Draft two detailed examples of 0-to-1 ML projects you led, focusing on your role in architecture, deployment, and scaling. Emphasize how you navigated ambiguous requirements and the specific impact of your design choices on production performance.
Review LLM orchestration and vector store architectures
Be prepared to discuss your experience with modern AI stacks, specifically how you have implemented retrieval-augmented generation (RAG) or managed long-term context in autonomous agents. Focus on trade-offs between different vector databases and orchestration frameworks.
Develop a framework for technical mentorship and standards
Prepare concrete examples of how you have raised the technical bar in previous teams without formal management authority. This should include your approach to conducting design reviews, establishing coding standards, and fostering a culture of rigorous experimentation.
Formulate questions on Customer Memory Team priorities
Prepare thoughtful questions regarding the team's current challenges with contextual intelligence, such as how they balance real-time inference latency with the need for deep personalization in multi-turn conversations.
Refine your approach to causal inference and experiment design
Review your experience with predictive modeling and experiment design, specifically for scenarios where randomization is partial. Be ready to explain how you apply causal or counterfactual methods to validate ML improvements in production environments.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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