Machine Learning Research Engineer

The group in Redmond is looking for a highly-qualified Machine Learning Research Engineer to build state-of-the-art tools for evaluating and understanding foundation models, with a focus of real-world uses of Artificial Intelligence (AI) and Machine Learning (ML). Our team conducts influential research published at top-tier venues in AI and ML (including NeurIPS, ICML, ICLR, CVPR, and FAccT) and works within Microsoft’s Responsible AI ecosystem to impact our AI-driven technologies such as Azure, Office, and Bing. 

We’re seeking an exceptional Machine Learning Research Engineer with demonstrated ability for technical work in the space of large foundational models with proficient coding and machine learning skills. This person is:

• Passionate about rigorous evaluation, understanding, and development of foundational models.  
• Motivated to make successful research methods accessible to the larger AI community through prototypes, open-source libraries, and development tools.  
• Proficient in design thinking and Object-Oriented Design, building clean, modular, maintainable and user-friendly open-source ML frameworks. 
• Experienced in measuring and maximizing the impact of open-source libraries. 

As a Machine Learning Research Engineer, you will play a crucial role in designing and developing impactful, high quality and well-engineered frameworks to empower the scientific evaluation, understanding, and development of foundational models. You will work closely with a team of passionate researchers and engineers to make sure such frameworks are compatible with modern cloud platforms, ML frameworks and libraries, model architectures, and various data modalities. You will also play a central role in defining and running large-scale experiments that contribute to our team’s research. 

We are looking for team players who are interested in developing next-generation platforms and tools for Machine Learning as well as conducting state-of-the-art research.Topics of interest include but are not limited to: rigorous evaluation and benchmarking, advances in AI interpretability, bias and fairness, and safety in real-world deployments. Our group takes a holistic approach to studying foundational models that includes a variety of data modalities (language, vision, multi-modal, and structured data) and modern model architectures. Candidates should demonstrate expertise in many of these aspects or show that they are interested in generalizing their skills into a variety of modalities and architectures.

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.

Required Qualifications 

• Bachelor's Degree in Computer Science, or related technical discipline AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python 
  • OR equivalent experience. 
• 3+ years coding proficiency in Python and familiarity with machine learning frameworks such as PyTorch, TensorFlow or scikit-learn. 
• Experience with training and evaluating machine learning models for Natural Language Processing (NLP), Vision, and/or Multimodal tasks. 
• 3+ years of industry experience involving cloud infrastructures and distributed ML environments. 

Other Requirements

• A current CV.  
• A brief cover letter attached to the CV is welcomed, but not mandatory. The letter may include 1-2 paragraphs indicating the applicant’s relevant expertise and interests, emphasizing potential connections and synergies between their previous experience and research areas described in this job call. The letter may also emphasize how the candidate thinks about the role of engineering in research for high-impact collaborative projects. 

Preferred Qualifications 

• Bachelor's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, or Python 
  • OR Master's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java or Python 
  • OR equivalent experience.
• Previous experience in design and development for research projects that fall into one of the following research areas: reliability & robustness of AI systems, rigorous evaluation and benchmarking, advances in AI interpretability, bias and fairness, and safety in real-world deployments. 
• Previous experience developing high impact open-source ML frameworks. 
• Well-established research record of publications in top-tier journals/conferences. 
• Effective communication skills. 
• Ability to work in a collaborative environment with multi-disciplinary teams.  
• Active participation in the scientific community. 

Software Engineering IC4 - The typical base pay range for this role across the U.S. is USD $117,200 - $229,200 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 $153,600 - $250,200 per year.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:

Microsoft will accept applications for the role until January 6, 2025.

#Research

• Collaborate with a dedicated research and engineering team to design and develop ML frameworks for model evaluation and understanding.  
• Define benchmarks and execute experiments for rigorous model evaluation and understanding. 
• System Design and Object-Oriented Design: Envision elegant solutions and craft scalable and efficient systems to drive the success of our ML frameworks. Develop clean, modular, and maintainable code to shape the foundation of our evaluation framework. 
• Work closely with partner engineering teams in both research and production.  
• Mentor or onboard incoming engineering contributors and empower them to maximize the team’s impact. 

• Embody our and .