Staff Machine Learning Engineer

See yourself at Twilio

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

Who we are 

At Twilio, we’re shaping the future of communications, all from the comfort of our homes. We deliver innovative solutions to hundreds of thousands of businesses and empower millions of developers worldwide to craft personalized customer experiences. Our dedication to remote-first work, and strong culture of connection and global inclusion means that no matter your location, you’re part of a vibrant, diverse team making a global impact each day. As we continue to revolutionize how the world interacts, we’re acquiring new skills and experiences that make work feel truly rewarding. Your career at Twilio is in your hands. 

About the job

Twilio is experiencing rapid growth and is seeking a Staff Machine Learning Engineer with a strong Data Science, Machine Learning & Predictive Modeling 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 Staff 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 & Gen AI-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 (Sage maker, Lambda, S3, Kendra), MySQL, Air table, 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 Zen desk 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 Gen AI 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 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 enterpr