Machine Learning Engineer

🕒 July 23

🇺🇸 United States – Remote

💵 $155.5k - $228.7k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 Machine Learning Engineer

🦅 H1B Visa Sponsor

This company has sponsored H1B visas in the past.

👻 Ghost score 13%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Twilio

WebsiteLinkedInAll Job Openings

5001 - 10000 employees

🔌 API

🤝 B2B

API • B2B

Twilio is a leading provider of cloud communications services that enables developers to build innovative communication solutions. Founded in 2008, Twilio has democratized access to communication channels such as voice, text, chat, video, and email through easy-to-use APIs. With headquarters in San Francisco and a global presence, Twilio empowers organizations of all sizes to engage effectively with their customers by integrating these communication capabilities into their applications.

📋 Description

• Drive innovation and develop cutting-edge products for developers, builders, and operators within Twilio’s Data & Observability Substrate organization • Develop, evaluate, and maintain scalable, low-latency, ML-based systems for real-time applications • Lead rapid research-to-production cycles translating business ideas into solutions for streaming anomaly detection, recommendation systems, predictive modeling, and agentic AI frameworks • Collaborate with engineers, architects, product managers, UI/UX designers, and ML/data science partners • 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 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 • Partner with Product, Data Science/ML, Engineering, and Security to deliver resilient, scalable, and compliant ML-powered services • Explain model and system design choices and their rationale • Own SLAs, on-call responsibilities, 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 • Travel occasionally for project or team in-person meetings

🎯 Requirements

• Strong foundation in ML/AI, including statistics, probability, and optimization • 5+ years of experience building, deploying, and operating data and ML systems in production • Proficiency in Python, Java, and SQL • Strong software engineering fundamentals, including system design, testing, version control, and code reviews • Hands-on experience with workflow orchestration and data pipelines, such as Airflow or Kubeflow • Experience with cloud data platforms and storage, such as SageMaker Feature Store, Snowflake, DynamoDB, or OpenSearch • Experience with ML lifecycle and MLOps tooling, such as MLflow, Metaflow, or SageMaker • Experience with LLM/agent frameworks such as LangChain or LangGraph • Working knowledge of Docker, Kubernetes, GitOps/CI/CD tools such as Argo CD, and at least one major cloud platform: AWS, GCP, or Azure • Understanding of data modeling, distributed computing, and streaming frameworks such as Spark/EMR, Flink, or Kafka Streams • Ability to ramp up quickly in new application and business domains • Strong written and verbal communication skills; ability to document and present designs and decisions • Comfort giving and receiving feedback in an Agile environment • Advanced degree in a relevant field is preferred, not required

🏖️ Benefits

• Competitive pay • Generous time off • Ample parental and wellness leave • Healthcare insurance • Retirement savings program • 401(k) retirement account • Paid sick time • Paid personal time off • Paid parental leave • Equity plan eligibility • Corporate bonus plan eligibility • Volunteering and donation support