Twilio

Staff Engineer, AI Security role focused on building autonomous defense for the AI lifecycle, including multi-agent frameworks, secure gateways, and real-time security gates. The role involves defining the MLSecOps roadmap and ensuring security for agentic workflows.

Principal Product Manager to lead product development for Console Copilot backend services and AI agent platforms. The role involves building foundational AI orchestration engines that power internal intelligent agents, ingesting Twilio-specific knowledge to provide proactive nudges and conversational assistance, and ensuring agents are performant, context-aware, and integrated into the customer journey. Key responsibilities include designing the 'Twilio Brain' knowledge layer, driving proactive engagement with AI nudges, ensuring omnichannel agent orchestration, optimizing agent accuracy via RAG and fine-tuning, cross-functional integration, and managing infrastructure costs and latency of LLM calls.

Staff Enterprise Security Engineer focused on building foundational security posture for Twilio's AI ecosystem, including technical guardrails and decision infrastructure for enterprise AI innovation. The role involves designing secure reference architectures, establishing AI vetting frameworks, and managing identity and posture for agentic AI.

Staff Data Scientist at Twilio focused on driving growth across the full funnel by revolutionizing cross-sell and upsell initiatives. The role involves designing data products, building recommendation engines, and leveraging ML/GenAI for Product-led-growth, with a strong emphasis on translating business objectives into a data science roadmap and measuring the impact of data products.

Principal Engineer to own the technical strategy for the Phone Numbers Platform, focusing on pioneering AI-driven development workflows using LLMs and agentic agents to accelerate code generation, automate architectural reviews, and synthesize complex global telecommunications regulations into executable logic. The role involves transitioning legacy systems, driving technical excellence, and mentoring engineers, with a strong emphasis on using AI to amplify team velocity and deliver features faster.

Machine Learning Engineer to drive innovation and develop cutting-edge ML-based systems for real-time applications, including anomaly detection, recommendation systems, predictive modeling, and agentic AI frameworks. This role involves designing, implementing, and maintaining scalable, low-latency ML solutions in production, building reproducible ML workflows, and implementing monitoring and evaluation frameworks.

Staff Software Engineer for the Enterprise AI Engineering team, focusing on building production-grade, full-stack AI applications and agentic solutions to transform customer support operations. The role involves leading the design and development of software infrastructure, integrating front-ends with backend services, and mentoring engineers.

Senior Offensive Security Engineer focused on testing AI/LLM systems, including prompt injection, jailbreaking, and building automated testing frameworks for sensitive data leakage. Also involves traditional offensive security tasks like penetration testing, network audits, and exploit development.

Staff Engineer, Offensive Security role focused on performing penetration testing, network audits, and AI/LLM probing. Responsibilities include designing attack chains, developing custom exploits, building automated testing frameworks for AI systems, and collaborating with security teams. Requires expertise in offensive security concepts, tooling, and scripting, with a focus on AI security tools and adversarial ML.

Senior Engineering Manager for Twilio's Trust Intelligence Platform, responsible for building and managing a team that develops advanced machine learning models and data pipelines for real-time risk prediction and decision-making in communications. The role involves strategic planning, partnering with product teams, ensuring operational excellence, and managing highly critical risk platform tools in the cloud.

Machine Learning Engineer responsible for designing, building, and operating cloud-native data and ML infrastructure for real-time intelligence, including data pipelines, feature stores, and ML workflows for training, evaluation, and inference.

Staff Machine Learning Engineer to scope, design, and deploy machine learning systems into production, partnering with Product & Engineering teams to execute the roadmap for Twilio’s AI/ML products and services. The role involves understanding customer needs, building scalable data products, and owning end-to-end execution of large-scale ML solutions.

Senior Engineer, Security Automation role at Twilio, focusing on developing and implementing AI/LLM-powered security solutions to automate critical security workflows, protect infrastructure, and enhance incident response. The role involves building data orchestration pipelines, designing LLM-driven applications for security analysis, and contributing to the AI roadmap.

Senior Engineer, Security Automation role at Twilio, focusing on developing and implementing AI-powered security solutions, automating security workflows, and building LLM-driven applications for security analysis and incident response. Requires strong software engineering background with experience in AI/ML, LLMs, data orchestration, and security principles.

This role focuses on building and operating an internal ML and data platform, including cloud-native pipelines, model-serving infrastructure, and developer tooling. It involves architecting scalable feature stores, streaming/batch pipelines, and low-latency model-serving layers on AWS, implementing MLOps best practices, and leading cross-functional engineering efforts.

Staff Machine Learning Engineer to design, build, and operate cloud-native data and ML infrastructure for Twilio's Trust Intelligence Platform. This role focuses on scalable data pipelines, feature stores, and ML training/evaluation/inference workflows, integrating event streams from various Twilio products to enable real-time intelligence. Requires strong software engineering fundamentals, cloud platform experience, and familiarity with ML lifecycle tooling.