Senior Applied Scientist
Microsoft
Senior Applied Scientist
Reading, Berkshire, United Kingdom
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Overview
The Microsoft Applied Sciences Group incubates disruptive technologies for Microsoft’s next-gen hardware products and is working on several exciting projects that will shape how computers and other devices perceive the user and the user’s environment. Operating as a dedicated applied science group within the company, this team works closely with several research and product teams to bring compelling new experiences to the market. Our team is at the forefront in redefining the PC with Copilot+—AI-powered Windows devices that are intelligent, secure, and efficient. With features like real-time translation and natural interaction, we’re building a faster, more intuitive, and human-centric computing future.
The team is growing, and we have an exciting opportunity for a talented Senior Applied Scientist to drive and lead implementation of state-of-the-art AI algorithms for specific and general-purpose silicon on next generation devices and operating systems. This is a hands-on team lead task. You will have collaboration opportunities throughout the organization and will be building new stuff that really works and has millions of users.
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.
Qualifications
Required Qualifications:
- Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND related experience (e.g., statistics predictive analytics, research)
- OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND related experience (e.g., statistics, predictive analytics, research)
- OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND related experience (e.g., statistics, predictive analytics, research)
- OR equivalent experience.
- Experience in deep learning and its different toolkits particularly Pytorch or Tensorflow.
- Record of publications in top-tier conferences or journals (CVPR, ICCV, ECCV, NeurIPS, ICLR).
- Experience in deep learning with familiarity with LLMs like Qwen, GPT, Llama or VLMs like Vision Transformer, CLIP, Florence, experience with different Image Encoders.
Preferred Qualifications:
- Experience in model quantization & optimization techniques such as GPTQ, LORA etc..
- Experience of using dataset curation, data generation using prompting state of art LLMs, automated model evaluation.
- Experience with model conversion and deployment frameworks like ONNX.
- Experience in network architecture search, quantization.
- Experience training foundation models, data collection and model distillation into smaller models.
- Experience with Parameter-Efficient Fine-Tuning methods for foundational models.
- Experience with distributed training libraries like DeepSpeed.
- Experience with Image encoders like Siglip, Florence, etc..
- Awareness or desire to learn about model compression and quantization techniques.
Responsibilities
- Research, design, and implement state-of-the-art, adapt or fine tune models destined for edge device inferencing for VLMs and LLMs.
- Collaborate with cross-functional teams, including researchers, engineers, and product teams, to integrate developed technologies into Microsoft’s products and services.
- Contribute to a real-time system involving multiple components.
- Mentor junior engineers and contribute to team knowledge-sharing sessions.
- Publish groundbreaking research results in top-tier conferences and journals, contributing actively to the scientific community.