Role overview
- Has solid experience with the deep technical aspects of research and engineering in Artificial Intelligence, Language Models, Machine Learning, or Speech Recognition.
- Can communicate clearly complex technical concepts, as well as product needs and priorities.
- Thrives in an environment where people collaborate across different disciplines, teams and geographies to deliver innovation to customers.
- Can inspire without authority across teams and motivate individuals to work together towards common goals.
- Can navigate uncertainty and create clarity and focus to execute towards a common vision.
Responsibilities
- Research, design, implement, adapt, train, fine tune or distil state-of-the-art multimodal models with the aim to support human communication in Teams.
- Prepare datasets, design and implement metrics and optimize model efficiency and performance.
- Collaborate closely with other groups (research, engineering and product groups) within the wider Microsoft organization, to create the next generation of AI innovation in our products and services.
- Embody Microsoft culture and values.
- Undergraduate or Master’s degree in Computer Science, Mathematics, Electrical or Computer Engineering, or related field.
- 2+ years practical ML Engineering and Python coding experience leveraging PyTorch, TensorFlow or similar framework, within large code repositories and in collaboration with additional team members.
- 2+ years’ practical experience in designing, training or fine-tuning transformer-based models or LLMs
- 2+ years’ experience of working with language, transcription, audio or multimodal applications (e.g., combining audio and video, text and audio).
- Excellent analytical, coding, communication, and collaborative skills.
Preferred qualifications
- PhD in Computer Science, Mathematics, Electrical or Computer Engineering, or related field.
- Industry experience delivering real-world solutions.
- Experience with large-scale distributed training and deployment.
- Experience with reinforcement learning of language models, quantization and other model optimization techniques.
- Experience with audio (speech to speech) foundation models, multimodal conversational models.
- Experience with AML/ADO pipelines and CI tools.
- System development skills spanning rapid prototypes to production systems with complex dependencies.
- A track record of innovation in AI evidenced by scientific publications, patents or contributions to product features
Tags & focus areas
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