Scientific Applications & AI Analyst

University of Toronto•Toronto, ON

8d•Onsite

About The Position

SciNet, within the VPRI portfolio, is a University of Toronto advanced research computing facility and support team. SciNet is installing a new AI capability with high-performance GPUs as part of the ISED Sovereign AI Compute initiative. The incumbent will support this platform and assist users in AI applications research and AI-assisted research. The Scientific Applications Analyst & AI Analyst provides senior IT services and training in GPU programming, data science applications, and scientific computing workflows for the SciNet advanced computing consortium. This role involves supporting users in designing, training, and validating machine learning models and deep learning algorithms; analyzing large datasets, performing data cleaning, transformation, and feature engineering; deploying AI solutions; and monitoring AI system performance. Additionally, the incumbent will assist users in deploying AI strategies for AI-assisted scientific research.

Requirements

Bachelor's Degree in Computer Science, Information Technology, Engineering, or quantitative sciences; or acceptable combination of equivalent experience.
Minimum eight years’ experience with advanced computing scientific applications or large-scale, data- driven scientific computations.
Significant experience and understanding of scientific numerical codes, compilers, code optimization, file input/output strategies, and data management.
Experience using data analysis and machine learning libraries.
Excellent knowledge of computing hardware and networking.
Solid knowledge of Fortran, C,C++ under Linux/Unix, and a scripting language like R and Python.
Demonstrated ability to program efficiently on serial, vector and parallel systems, with the ability analyze and improve software codes and interface well with researchers across all disciplines on their computing needs.
Excellent verbal and written communication skills with the ability to communicate highly technical terms and concepts to people of non-IT background.

Nice To Haves

Visualization expertise and presentation/training skills an asset.

Responsibilities

Analyzing, recommending, and designing highly complex software architecture
Developing and updating architectural framework for highly complex and confidential university-wide applications
Writing complex technical code
Analyzing and making recommendations for programming enhancements
Evaluating programming code to ensure it has validity, compatibility, and that it meets appropriate standards
Providing formal job-related training
Building and strengthening relationships with stakeholders and partners of strategic importance
Acting as an expert resource to developers on architecture, technical standards and coding techniques
Support the new AI capability platform and assist users in AI applications research, and in AI-assisted research in general.
Support users in designing, training, and validating machine learning models and deep learning algorithms.
Analyze large datasets, perform data cleaning, transformation, and feature engineering to prepare data for model training.
Deploy AI solutions into production environments, ensure they integrate seamlessly with existing software applications and infrastructure.
Monitor the performance of AI systems and make adjustments to improve efficiency, accuracy and scalability.
Assist users (clients) in deployment of AI strategies for AI-assisted scientific research.