AI Applied Scientist / AI Applied Scientistess

Overview

The Spatial AI Lab is part of the Applied Sciences Group, a Microsoft research and development organization dedicated to creating next-generation human-computer interaction technologies. We leverage the latest advancements in AI while exploring new hardware capabilities and device form factors. Our team of scientists and engineers possesses robust expertise in computer vision, multi-modal AI, as well as spatial and embodied AI.

In this role, you will be instrumental in developing intelligent systems for innovative agent types through the training and enhancement of multimodal AI models. This position will provide you with valuable experience in building and utilizing AI models for Microsoft products, along with large-scale AI systems. You will also have the chance to engage in cutting-edge research, collaborating with esteemed partners such as ETH Zurich, publishing in top-tier venues, presenting at workshops, and mentoring students.

At Microsoft, our mission is to empower every individual and organization on the planet to achieve more. As employees, we unite with a growth mindset, innovate to empower others, and work collaboratively to achieve our common objectives. Each day, we build upon our values of respect, integrity, and accountability to foster a culture of inclusion, where everyone can thrive in both their professional and personal lives.

Responsibilities

• Research novel machine learning algorithms and models.
• Engage in the pre and/or post-training of foundational multimodal models.
• Develop data and learning solutions for enhanced scalability, efficiency, and performance.
• Curate training and evaluation datasets/benchmarks.
• Optimize models for CPUs, GPUs, and NPUs and integrate them into products.
• Collaborate across Microsoft research and engineering teams.

Qualifications

Required Qualifications:

• A PhD in Machine Learning/Computer Vision or a minimum of 3 years of relevant industry experience.
• Proficiency in programming languages such as Python and/or C++.
• Hands-on experience with modern deep learning frameworks (e.g., PyTorch, TensorFlow, Jax).
• A self-motivated team player with strong problem-solving skills and eagerness to learn.
• Ability to present complex technical concepts to a diverse audience.

Preferred Qualifications:

• Experience in one or more of the following areas:
• Hands-on experience with multimodal models in any of the following topics:
• Pre and/or post-training of large vision-language models;
• Skills in techniques such as pruning, distillation, and fine-tuning;
• Experience with large language models (LLMs);
• Expertise in large vision-language models (VLMs);
• Involvement with video generative models and diffusion algorithms;
• Familiarity with action-based transformers and Vision Language Action models (VLAs).
• Experience with large-scale machine learning compute systems.
• Proven track record of impactful publications in top-tier machine learning or computer vision conferences (NeurIPS, ICML, CVPR, ECCV, ICCV) or significant contributions to successful industry initiatives.

This position will be open for a minimum of 5 days. Applications will be accepted on an ongoing basis until the position is filled.

Apply online using the form below. Please note that only applications matching the job profile will be considered.

Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations, and ordinances. If you need assistance with religious accommodations and/or reasonable accommodations due to a disability during the application process, please refer to our page on requesting accommodations.