Principal Research Engineer – Generative AI – AI Frontiers

Redmond, Washington, United States

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The AI Frontiers lab at Microsoft Research is charted with ambitious research goals for advancing Artificial Intelligence (AI) capabilities in several key areas including modeling, algorithms, reasoning and agentic AI. Our lab offers a vibrant environment for cutting-edge multidisciplinary research, including an open publication policy and close links to top academic institutions around the world.

 

This Principal Research Engineer position is a unique opportunity to contribute towards tackling some of the hardest and most rewarding challenges in AI. You will help develop novel ideas in bleeding edge reinforcement learning research as well as help evolve our pre-training, mid-training, and post-training codebases that gave birth to famous models such as Phi establishing many new records. You will collaborate with researchers and engineers across many disciplines to help advance the state of the art in reasoning and agentic AI.

 

Our lab provides opportunities for experimentation and access to diverse array of real-world problems and data along with potential to ship your research to over billion Microsoft customers. At AI Frontiers we strive to expand the pareto frontier of AI capabilities, efficiency, and safety through innovations in foundation models and learning agent platforms. Our projects include Phi, Orca, AutoGen, Eureka, OmniParser, Magentic-One, Magentic-UI, Dion, Belief State Transformers among many others.

   

Our ongoing research areas encompass but are not limited to:

  • Pre-training: language models, action models and multimodal models 
  • Mid-training: long context extension 
  • Post-training: e.g., Instruction tuning and reinforcement learning from feedback  
  • Reasoning: Enabling LLMs to scale inference time compute via reinforcement learning  
  • Action Models: Trailing models capable of taking action in the digital world (e.g. computer use and web agents) 
  • Orchestration and multi-agent systems: automated orchestration between multiple agents incorporating collaboration, human feedback and oversight  

If your skills and interests intersects any of these areas, please apply today and join us in this amazing journey!

 

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.

In alignment with our Microsoft values, we are committed to cultivating an inclusive work environment for all employees to positively impact our culture every day.

Responsibilities

  • As a Principal Research Engineer in AI Frontiers, you will design, develop, execute, and implement technology research projects in collaboration with other researchers, engineers, and product groups. 
  • As a member of a world-class research organization, you will be a part of research breakthroughs in the field and play a crucial role in developing, improving, and exploring the capabilities of Large Language Models (LLMs), reasoning and agentic AI.
  • Embody our culture and values.

Qualifications

Required Qualifications

  • Bachelors in Computer Science or relevant field AND 6+ years related experience  
    • OR Master's Degree in Computer Science or related field AND 4+ years related experience 
    • OR Doctorate in Computer Science or related field AND 3+ years related experience  
    • OR equivalent experience.
  • 1+ year(s) experience developing with Python and Pytorch/JAX. 

Preferred Qualifications

  • Familiarity with architecture and optimizations for large language models.
  • Hands-on work in debugging and profiling Pytorch distributed code.
  • Basic understanding of working of CUDA kernels.
  • Familiarity with pre-training, mid-training and/or post-training pipelines for language and/or multimodal models. 
  • Foundational understanding of reinforcement learning and key challenges in the field.
  • Experience with verl, Ray, Megatron and/or vLLM is a significant plus.
  • Any experience in building scalable services can be highly complementary.

Applied Sciences IC5 - The typical base pay range for this role across the U.S. is USD $139,900 - $274,800 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $188,000 - $304,200 per year.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: https://careers.microsoft.com/us/en/us-corporate-pay

Microsoft will accept applications and processes offers for these roles on an ongoing basis.

 

Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances.  We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process, please send a request via the Accommodation request form.

 

Benefits/perks listed below may vary depending on the nature of your employment with Microsoft and the country where you work.

 

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Tags: Architecture Computer Science CUDA Generative AI JAX LLMs Pipelines Python PyTorch Reinforcement Learning Research Transformers vLLM

Perks/benefits: Career development Medical leave

Region: North America
Country: United States

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