Machine Learning Engineer

Details

• Partner with product, UX, and technical stakeholders to analyze business problems, clarify requirements, define scope, and translate them into measurable ML problem statements.
• Design, implement, and maintain scalable, enterprise-grade ML solutions in production.
• Build reproducible ML workflows for data preparation, training, evaluation, and inference using modern orchestration and MLOps tooling.
• Implement monitoring and evaluation frameworks to continuously improve data quality, model performance, latency, and cost through feedback loops.
• Partner cross-functionally with Product, Data Science/ML, Engineering, and Security to deliver resilient, scalable, and compliant ML-powered services.
• Demonstrate end-to-end systems understanding and articulate the “why” behind model and system design choices.
• Own operational excellence: SLAs, on-call, incident response, customer feedback triage, and blameless post-mortems.
• Drive engineering excellence via AI-assisted SDLC, code reviews, automated testing, MLOps best practices, knowledge-sharing, and mentoring.
• Actively adopt AI-assisted practices to improve implementation and collaboration efficiency.
• Strong foundation in ML/AI (statistics, probability, optimization) with the ability to apply these concepts to real-world problems.
• 5+ years of experience 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) and cloud data platforms/storage (e.g., SageMaker Feature Store, Snowflake, DynamoDB, OpenSearch).
• Experience with the ML lifecycle and MLOps tooling (e.g., MLflow, Metaflow, SageMaker; LLM/agent frameworks such as LangChain/LangGraph; model evaluation/observability tools such as Galileo or similar).
• Working knowledge of containerization and cloud infrastructure, including Docker and Kubernetes, GitOps/CI/CD tools (e.g., Argo CD), and at least one major cloud platform (AWS, GCP, or Azure).
• Understanding of data modeling and scalable systems, including distributed computing and streaming frameworks (e.g., Spark/EMR, Flink, Kafka Streams); familiarity with GPU-based implementation is a plus.
• Demonstrated ability to ramp up quickly and operate effectively in new application/business domains.
• Strong written and verbal communication skills: able to document and present designs and decisions, and comfortable giving/receiving feedback in an Agile environment.
• Familiarity with ML problem areas and techniques, including recommendation systems (e.g., graph-based approaches, two-tower models), time-series modeling (classical and deep learning), representation learning (e.g., embeddings), anomaly detection, and causal inference.
• Practical experience with LLMs and generative AI workflows, including foundation model fine-tuning, RAG, and vector databases.
• Evidence of technical leadership/impact, such as contributions to open-source data/ML projects and/or published technical presentations, blog posts, papers, or research.
• Domain experience (plus) in communications, marketing automation, or customer engagement analytics.
• Familiarity with AI-assisted development tools (e.g., Claude, GitHub Copilot/Codex, Cursor, etc.).
• Advanced degree preferred (M.S. or Ph.D.) in a relevant field.
• Based in Colorado, Hawaii, Illinois, Maryland, Massachusetts, Minnesota, Vermont or Washington D.C. : $155,520.00 - $194,400.00.
• Based in New York, New Jersey, Washington State, or California (outside of the San Francisco Bay area): $164,640.00 - $205,800.00.
• Based in the San Francisco Bay area, California: $182,960.00 - $228,700.00
• This role may be eligible to participate in Twilio’s equity plan and corporate bonus plan. All roles are generally eligible for the following benefits: health care insurance, 401(k) retirement account, paid sick time, paid personal time off, paid parental leave.