Machine Learning Engineer

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 

Twilio values diverse experiences from all kinds of industries, and we encourage everyone who meets the required qualifications to 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 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

Desired:

• Familiarity with using LLMs (OpenAI, Claude, Gemini, Llama etc.), RAG, Agents, Model Fine-tuning, Few-shot prompting, Prompt Engineering.
• Experience with Python-specific frameworks such as Llamaindex, Langchain, Streamlit, Gradio, FastHTML, Chainlit etc.
• Research background & experience writing papers for top-tier peer-reviewed conferences or journals

Location

This role will be remote based in Canada, but is only eligible to be hired in the provinces of Ontario, Alberta and British Columbia.