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This posting is for a new vacancy.Minimum qualifications:
Please note that the compensation details listed in Canada role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google .
Responsibilities
- Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience.
- 3 years of experience in a data science role, with a focus on Machine Learning (ML) and Natural Language Processing (NLP) for developing and deploying AI/ML solutions.
- Experience with relevant AI/ML libraries (e.g., TensorFlow, PyTorch, scikit-learn, Hugging Face).
- PhD degree in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field.
- Experience with Large Language Models (LLMs), including their application in solving business problems.
- Experience in intelligent autonomous agents, including their design, development, evaluation, and deployment.
- Experience in customer support or support-adjacent role.
- Understanding of cloud platforms (e.g., Google Cloud Platform) and their AI/ML services, particularly those related to LLMs and generative AI.
- Excellent programming skills in Python or a similar language with the ability to translate data into actionable insights and communicate findings to technical and non-technical stakeholders.
Please note that the compensation details listed in Canada role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google .
Responsibilities
- Develop predictive, personalized, and proactive customer support solutions to drive customer success at scale while researching and integrating advancements in Large Language Models (LLM), generative AI, and AI agent architectures to continuously enhance the capabilities and foster innovation.
- Lead the development and deployment of advanced AI/ML solutions, with an emphasis on LLMs and intelligent autonomous agents, addressing business issues.
- Implement evaluation frameworks and metrics for LLMs and AI agents, encompass both traditional model performance and agent-specific evaluation criteria (eg. task completion rate, reasoning quality).
- Monitor and maintain deployed LLM and AI agent solutions in production, including tracking key performance indicators, identify and address model drift, and ensure system stability and scalability.
- Identify and define AI/ML opportunities by collaborating with stakeholders to translate business needs into technical requirements and measurable outcomes.
Key Skills
Ranked by relevance
ai
machine learning
cloud
natural language processing
artificial intelligence
google cloud platform
tensorflow
pytorch
python
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- Posted
- Jan 15, 2026
- Type
- Full-time
- Level
- Not Applicable
- Location
- Waterloo
- Company
Industries
Information Services
Technology
Information
Internet
Categories
General Business
Strategy/Planning
Consulting
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