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Huawei Canada has an immediate 12-month internship opening for an Associate Engineer.
About the team:
The Intelligent Complex Systems Team, currently a part of the Waterloo Research Centre, examines recent advancements in artificial intelligence (AI) and robotics to determine its potential for broader applications. This innovative team researches AI challenges such as matching human capabilities and ensuring the safety of collaborative AI systems.
About the job:
Conduct research and development on runtime assurance techniques for AI/LLM-enabled systems.
Design and implement a runtime assurance framework in Python to monitor, validate, and mitigate AI model uncertainties.
Develop and integrate uncertainty quantification, anomaly detection, and robustness evaluation techniques for LLMs and AI models.
Explore retrieval-augmented generation (RAG), AI observability frameworks, and runtime monitoring mechanisms for LLM-based decision-making with a focus on reducing hallucinations and improving factual consistency.
Conduct experimental evaluations to improve system robustness, performance, and adaptability.
Collaborate with AI/ML researchers and engineers to integrate runtime assurance techniques into AI development pipelines.
Conduct research and development on runtime assurance techniques for AI/LLM-enabled systems.
Conduct research and development on runtime assurance techniques for AI/LLM-enabled systems.
Design and implement a runtime assurance framework in Python to monitor, validate, and mitigate AI model uncertainties.
Design and implement a runtime assurance framework in Python to monitor, validate, and mitigate AI model uncertainties.
Develop and integrate uncertainty quantification, anomaly detection, and robustness evaluation techniques for LLMs and AI models.
Develop and integrate uncertainty quantification, anomaly detection, and robustness evaluation techniques for LLMs and AI models.
Explore retrieval-augmented generation (RAG), AI observability frameworks, and runtime monitoring mechanisms for LLM-based decision-making with a focus on reducing hallucinations and improving factual consistency.
Explore retrieval-augmented generation (RAG), AI observability frameworks, and runtime monitoring mechanisms for LLM-based decision-making with a focus on reducing hallucinations and improving factual consistency.
Conduct experimental evaluations to improve system robustness, performance, and adaptability.
Conduct experimental evaluations to improve system robustness, performance, and adaptability.
Collaborate with AI/ML researchers and engineers to integrate runtime assurance techniques into AI development pipelines.
Collaborate with AI/ML researchers and engineers to integrate runtime assurance techniques into AI development pipelines.
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