Overview

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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 leveraging the most recent AI developments and exploring new hardware capabilities and device form-factors. Our team of scientists and engineers has strong expertise in computer vision, multi-modal AI, spatial and embodied AI.  

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As ML Engineer you will work closely with several research and product teams to bring compelling new experiences to the market. A lot of these experiences will be powered by computer vision and multimodal AI models. You may work on collecting data, evaluating and training models, and writing production quality code. You will also have the opportunity to join cutting edge research working with partners like ETH Zurich to publish in top-tier venues, present at workshops, and mentor students. 

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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. 

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‍Responsibilities

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• Implement algorithms, design model architectures, run experiments, perform evaluation, build data pipelines  

• Build data and learning solutions for scalability, efficiency, and performance. 

• Curate training and evaluation datasets/benchmark.

• Optimize deep neural networks for deployment on Neural Processing Units (NPUs), GPUs and cloud environments, maximizing efficiency and performance.

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‍Qualifications‍

Required Qualifications: 

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• A Master in Computer Science or 3+ years of relevant industry experience. 

• Engineering skills in programming languages such as Python and/or C++.   

• Hands-on experience with modern deep learning frameworks (e.g. Pytorch/Tensorflow/Jax). 

• Self-motivated team-player, problem solver, and keen to learn.   

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Preferred Qualifications: 

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• Ability to present complex technical concepts to diverse audiences.

• Experience quantizing deep neural networks for NPUs and GPUs. Familiarity with ONNX format.

• Hands-on experience in training and fine-tuning deep neural networks.

• Knowledge of distributed computing frameworks (e.g., Ray) and experience using Azure Machine Learning (AzureML) for model training and deployment.

• Background in building agent-based systems.

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