Machine Learning Engineer

Role overview

Twilio is hiring a Machine Learning Engineer for its Data & Observability Substrate organization. This is a hands-on, builder-focused role bridging Product, Design, and Engineering to build, evaluate, and maintain scalable, low-latency ML systems for real-time applications.

Responsibilities

• Partner with product, UX, and technical stakeholders to analyze business problems, clarify requirements, define scope, and translate needs into measurable ML problem statements.
• Design, implement, and maintain production-ready, enterprise-grade ML solutions.
• Build reproducible ML workflows for data preparation, training, evaluation, and inference using orchestration and MLOps tooling.
• Implement monitoring and evaluation frameworks to improve data quality, model performance, latency, and cost via feedback loops.
• Collaborate with cross-functional teams (Engineering, Data Science/ML, Product, and Security) to deliver resilient, scalable, compliant ML services.
• Communicate end-to-end system design decisions, including the “why” behind architecture and model choices.
• Own operational excellence: SLAs, on-call, incident response, customer feedback triage, and blameless post-mortems.
• Drive engineering excellence through AI-assisted SDLC, code reviews, automated testing, MLOps best practices, knowledge sharing, and mentoring.
• Adopt AI-assisted practices to improve implementation and collaboration efficiency.

Required qualifications

• Strong foundation in ML/AI (statistics, probability, optimization) applied to real-world problems.
• 5+ years building, deploying, and operating data and ML systems in production.
• Proficient in Python, Java, and SQL; strong software engineering fundamentals (system design, testing, version control, code reviews).
• Hands-on experience with workflow orchestration and data pipelines (e.g., Airflow, Kubeflow).
• Experience with cloud data platforms/storage (e.g., SageMaker Feature Store, Snowflake, DynamoDB, OpenSearch).
• Experience across the ML lifecycle and MLOps tooling (e.g., MLflow, Metaflow, SageMaker and LLM/agent frameworks).

Nice-to-have / implied strengths

• Experience delivering real-time ML solutions such as streaming anomaly detection, recommendation systems, predictive modeling, and/or agentic AI frameworks.
• Proven ability to lead research-to-production cycles.