Twilio

Build the future of communications.

Staff Machine Learning Engineer, L4

Machine Learning EngineerMachine Learning EngineerFull TimeRemoteTeam 5,001-10,000H1B SponsorCompany SiteLinkedIn

Location

California + 5 moreAll locations: California, Connecticut, New Jersey, New York, Pennsylvania, Washington

Posted

1 day ago

Salary

$188.2K - $276.7K / year

5 yrs expEnglishAWSKerasMy SQLNumpyPandasPythonScikit LearnSQL

Job Description

• Develop and Deploy AI/ML Models: Build and deploy machine learning models by leveraging NLP, recommendation systems & GenAI-powered applications, to production environments, ensuring they meet the diverse needs of Twilio's verticals and customer base. • Collaborate Across Teams: Work closely with product, program, analytics, and engineering teams to implement and refine machine learning, statistical, and forecasting models that drive business outcomes. • Utilize Advanced Technical Stack: Leverage our technical stack, including Python, SQL, R, AWS (Sagemaker, Lambda, S3, Kendra), MySQL, and libraries such as Pandas, NumPy, SciKit-Learn, XGBoost, Matplotlib, and Keras, to develop robust and scalable AI/ML solutions. • Integrate Enterprise Data Sources: Effectively utilize enterprise data sources like Salesforce and Zendesk to inform model development and enhance predictive accuracy. • Harness the Power of LLMs: Apply knowledge of Large Language Models (LLMs) such as OpenAI's GPT models, Claude, Gemini, Llama, Whisper, and Groq to develop innovative GenAI use cases and solutions.

Job Requirements

  • 5+ years of applied ML engineering experience
  • Develop and Deploy AI Models: Build and deploy machine learning models leveraging NLP techniques and GenAI-powered applications, to production environments, ensuring they meet the diverse needs of Twilio's verticals and customer base.
  • Collaborate Across Teams: Work closely with product, program, analytics, and engineering teams to implement and refine machine learning, statistical, and forecasting models that drive business outcomes.
  • Utilize Advanced Technical Stack: Leverage our technical stack, including Python, SQL, R, AWS (Sagemaker, Lambda, S3, Kendra), MySQL, Airtable, and libraries such as Pandas, NumPy, SciKit-Learn, XGBoost, Matplotlib, and Keras, to develop robust and scalable AI/ML solutions.
  • Integrate Enterprise Data Sources: Effectively utilize enterprise data sources like Salesforce and Zendesk to inform model development and enhance predictive accuracy.
  • Harness the Power of LLMs: Apply knowledge of Large Language Models (LLMs) such as OpenAI's GPT models, Claude, Gemini, Llama, Whisper, and Groq to develop innovative GenAI use cases and solutions

Benefits

  • Health care insurance
  • 401(k) retirement account
  • Paid sick time
  • Paid personal time off
  • Paid parental leave

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