Microsoft Machine Learning Research Engineer 

United States, Washington 

422811594

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.

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.

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

• 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 .