Machine Learning Engineer

See yourself at Twilio

Join the team as our next Machine Learning Engineer on Twilio’s Efficiency Engineering team.

Who we are & why we’re hiring

Twilio powers real-time business communications and data solutions that help companies and developers worldwide build better applications and customer experiences.

Although we're headquartered in San Francisco, we have presence throughout South America, Europe, Asia and Australia. We're on a journey to becoming a global company that actively opposes racism and all forms of oppression and bias. At Twilio, we support diversity, equity & inclusion wherever we do business.

About the job

Twilio is experiencing rapid growth and is seeking a Machine Learning Engineer with a strong Data Engineering, Machine Learning background to expand and enhance our Efficiency Engineering team's AI function. This team is responsible for developing cutting edge solutions leveraging AI & Data Science for external and internal customers. 

As a Machine Learning Engineer within Efficiency Engineering, you will be instrumental in developing supervised machine learning (ML) propensity models as well as state-of-the-art GenAI/LLM-powered applications. Your solutions will cater to the dynamic needs of Twilio’s diverse verticals and our extensive customer base. This role offers an exciting opportunity to harness the power of advanced machine learning technologies to drive innovation and efficiency.

As a member of this team, you will have the opportunity to work on groundbreaking projects that directly impact the efficiency and success of our customers. You will collaborate with talented professionals who are as driven and enthusiastic about AI and Machine Learning as you are. Your contributions will be crucial in shaping the future of AI at Twilio, and you will have the support and resources needed to innovate and excel.

Responsibilities

In this role, you’ll:

• 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, OpenSearch), 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.

Qualifications 

Not all applicants will have skills that match a job description exactly. Twilio values diverse experiences in other industries, and we encourage everyone who meets the required qualifications to apply. While having “desired” qualifications make for a strong candidate, we encourage applicants with alternative experiences to also apply. If your career is just starting or hasn't followed a traditional path, don't let that stop you from considering Twilio. We are always looking for people who will bring something new to the table!

Required:

• 5+ years of data engineering, applied ML & software engineering experience
• Develop and Deploy AI pipelines & models: Build and deploy data pipelines & 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, OpenSearch), MySQL, Airtable, Pinecone 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