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Senior Applied Research Scientist - Jobs in Montréal

ServiceNow
Job LocationMontréal
EducationNot Mentioned
SalaryNot Mentioned
IndustryNot Mentioned
Functional AreaNot Mentioned
Job TypePermanent

Job Description

Job DescriptionTeam The Foundation Models Lab at ServiceNow Research builds the foundation for ServiceNow’s bespoke generative AI solutions, specifically tailored to meet the unique demands of our enterprise environment. Renowned for co-leading the BigCode project and releasing influential models like StarCoder and StarCoder 2, along with datasets such as the Stack and the Stack v2, our lab is now dedicated to strategic pretraining of foundation models for enterprise applications. Our mission extends beyond mere training; we aim to holistically design large language models considering their entire lifecycle. This includes curating training data to meet specific performance expectations, maximizing training efficiency, and, crucially, optimizing models for fast and cost-effective inference. By doing so, we ensure that our AI models are robust, efficient, and ready for further refinement and integration into ServiceNow’s broader AI ecosystem. Role We are seeking a Senior Applied Research Scientist with a passion for deep learning, GPU programming, and optimizing computational models to the maximum. In this role, you will:

  • Enable large-scale model training projects that aim to significantly enhance our AI capabilities and ensure they are perfectly adapted to our platform.
  • Take a lead in enhancing our 3D-parallelism training framework, aiming to make it more feature-complete and further increasing its speed and efficiency. This involves identifying compute bottlenecks and implementing critical tensor operations using OpenAI’s Triton JIT compiler to maximize GPU utilization and training throughput.
  • Design, implement, and optimize novel language model architectures that prioritize inference speed and scalability, aligning with ServiceNow’s operational needs.
  • Contribute to broadening our training framework’s support for a diverse range of model architectures, balancing maintainability and stability with the need for innovative features and experimentation.
  • Contribute to the open-sourcing efforts of our frameworks by ensuring they are well-documented, user-friendly, and ready for external adoption.

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