Senior Applied Scientist - LLM
Microsoft
Senior Applied Scientist - LLM
Vancouver, British Columbia, Canada
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Overview
Get the thrill and excitement of building media applications! While backed by the organization that powers Microsoft Teams and Azure Communication Services, we are building a new real-time communication platform from the ground, built for Agent-first, AI-ready communications, breaking the mold of the traditional "conference call” and advancing standards for Agentic communications.
We are looking for a Senior Data & Applied Scientist to join our team. As an Applied Scientist, you will play a pivotal role in transforming traditional media application with AI powers. You’ll be developing scalable algorithms and machine learning pipelines, and use large language model to drive innovation with realtime media and producing grounding data for intelligence processing. This is a unique opportunity to work at the intersection of platform engineering and applied science, helping shape the future of AI-powered productivity.
Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.
Qualifications
Required Qualifications:
- Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics predictive analytics, research)
- OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)
- OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research)
- OR equivalent experience.
- 4+ years of experience in applied machine learning, with a focus on LLMs, agent systems, or reinforcement learning.
Other Requirements:
Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include but are not limited to the following specialized security screenings:
- Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.
Preferred Qualifications:
- Hands-on experience with model training pipelines using PyTorch, TensorFlow, JAX, or similar frameworks.
- Familiarity with distributed training, prompt engineering, evaluation strategies, and model deployment best practices.
- Coding and debugging skills, and comfort working in cross-functional, agile environments.
Applied Sciences IC4 - The typical base pay range for this role across Canada is CAD $114,400 - CAD $203,900 per year.
Find additional pay information here:
https://careers.microsoft.com/v2/global/en/canada-pay-information.html
Microsoft will accept applications for the role until September 19th.
#realtime #media #AI #LLM #AppliedScientist
Responsibilities
- Drive data exploration and analysis by collecting initial datasets, selecting appropriate analytical techniques, and applying foundational statistical methods to extract insights.
- Leverage generative AI and agentic orchestration to build intelligent systems that powers multi modality media processing.
- Design and implement advanced LLM-based architectures and agentic systems for real-world product scenarios.
- Apply and adapt research ideas to solve practical challenges in reasoning, planning, memory, and alignment.
- Collaborate with cross-functional teams—including product, engineering, and research—to align efforts and deliver high-impact solutions.
- Champion customer-centric solutions by understanding business goals, identifying growth opportunities, and managing expectations throughout the project lifecycle.