- Developing innovative machine learning models for network optimization, prediction, and troubleshooting.
- Applying expertise in Graph ML, Classic ML (regression/classification), Deep Learning, Causality, and Optimization techniques.
- Develop and utilize mathematical models to solve complex network problems.
- Conducting research to develop models and validate their feasibility, performance, and relevance.
- Ensuring interpretability and usability of the models for network optimization and troubleshooting.
- Transform validated models into modular features, ensuring they meet the set engineering criteria
- Participating in the iterative process of model development, including framing problems, developing hypotheses, designing and conducting experiments, and synthesizing findings.
- Develop and execute automated tests to ensure the quality, functionality, and compatibility of new features.
- Work closely with the Engineering Team to address any issues or concerns during the integration process.
- Communicating the progress, challenges, and needs during the model development process to the Engineering Program Manager.
- PhD or Master's degree in Computer Science, Data Science, Artificial Intelligence, or a related field.
- 5+ years of proven experience as a Data Scientist, Machine Learning Developer or similar role. At least 2 years of experience building ML Systems.
- Solid understanding of machine learning principles, algorithms, and applications.
- Expertise in Graph ML, Classic ML (regression/classification), Deep Learning, Causality, and Optimization techniques.
- Proficiency in Python and experience with libraries such as TensorFlow, PyTorch, Scikit-learn, and Dask.
- Experience with distributed machine learning and proficiency in using Dask for parallel computing.
- Excellent understanding of data structures, data modeling.
- Excellent problem-solving skills, attention to detail, and ability to work in a team-oriented environment.
- Strong communication skills to effectively collaborate with team members and report to management.
- Experience with networks is a significant asset.
- Experience with graph databases, specifically Neo4j, for network topology and service modelling is a significant asset.
- Demonstrates a 'can-do' attitude, always seeking innovative solutions, and pushing boundaries.
- Maintains a startup mindset, thriving in a dynamic, fast-paced environment, and contributing positively to team energy.
- Understands that the goal is not to use the most complex algorithm but to bring real value to our users. We are looking for a Machine Learning Scientist who is passionate about pushing the boundaries of what is possible. If you're driven by curiosity, love problem-solving, and want to make a significant impact on a revolutionary project, we'd love to hear from you.
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Senior Machine Learning Engineer - Toronto, Canada - Extreme Networks
Description
Extreme Networks Named to Computerworld's 2023 List of Best Places to Work in IT Over 50,000 customers globally trust our end-to-end, cloud-driven networking solutions and rely on our top-rated services and support to accelerate their digital transformation efforts and deliver progress like never before and with double digit growth year over year, no provider is better positioned to deliver better outcomes on scale, than Extreme.We believe in "walking the walk" of our strong core values which enable us to successfully advance together. Diversity and Inclusion is a vital part of our values and beliefs, and we're proud to foster an environment where every Extreme employee can thrive. Come become part of something big with us We are a global leader, with hubs in North America, South America, Asia Pacific, Europe, and the Middle East.Introduction: Extreme Networks is on the cutting edge of technology, pushing boundaries and creating revolutionary networking experiences. We are seeking a versatile and innovative Machine Learning Scientist to join our Core Modelling Team. The successful candidate will work on ground-breaking projects aiming to optimize network planning, design, security, operations, and support through real-time event detection, future occurrence prediction, scenario planning, and configuration optimization.Responsibilities
Qualifications:
Desired Qualities