Senior Applied Scientist

Microsoft; Ashford, Reading; On-site; GBP 60,000 - 85,000; Full time

Job summary

A leading technology company seeks a research scientist to develop advanced generative models for image and video applications. The role involves optimizing deep neural networks for deployment on specialized hardware, collaborating with various teams, and publishing research findings. Ideal candidates will have a strong background in statistics, computer science, and experience with generative AI solutions.

Qualifications

• Experience developing and optimizing generative models for image and video.
• Proven track record with neural network architectures for NPUs.

Responsibilities

• Research and implement generative models for image and video generation.
• Collaborate with teams to integrate technologies into products.

Skills

Python

Deep Learning

Statistical Analysis

Communication

Education

Bachelor's Degree in Statistics

Master's Degree in Computer Science

Doctorate in Electrical Engineering

Tools

PyTorch

TensorFlow

Job description

Responsibilities (Text Only)

- Research, design, and implement state-of-the-art generative models (diffusion, auto-regressive, etc.) for high-quality image and video generation. - Optimize deep neural networks for deployment on Neural Processing Units (NPUs), maximizing efficiency and performance. - Collaborate with cross-functional teams, including researchers, engineers, and product teams, to integrate developed technologies into Microsoft’s products and services. - Publish groundbreaking research results in top-tier conferences and journals, contributing actively to the scientific community. - Mentor junior scientists and interns, fostering a collaborative and innovative research environment.

Qualifications (Text Only)

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. - Proven experience developing and optimizing generative models, particularly diffusion and auto-regressive models, for image and video applications. - Strong track record of optimizing neural network architectures specifically for NPUs or other hardware accelerators. - Proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow). - Excellent analytical, communication, and collaborative skills. Preferred Qualifications: - Industry experience delivering real-world generative AI solutions. - Knowledge of hardware-aware model optimization techniques. - Experience with large-scale distributed training and deployment. - Experience with AML/ADO pipelines. - Knowledge of ML model optimization techniques. - Demonstrated ability to publish impactful research in leading AI/ML conferences such as CVPR, ICCV, NeurIPS, ICML, or similar. Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances.If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process, please send a requestvia the Accommodation request form. Benefits/perks listed below may vary depending on the nature of your employment with Microsoft and the country where you work. #W+Djobs