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- Tasks: Research and implement cutting-edge generative models for image and video creation.
- Company: Join Microsoft, a leader in technology innovation and research.
- Benefits: Enjoy flexible work options, competitive pay, and unique corporate perks.
- Why this job: Be part of a collaborative team making impactful contributions to AI and the scientific community.
- Qualifications: Bachelor's or higher in relevant fields with experience in generative models and deep learning.
- Other info: Mentorship opportunities available for junior scientists and interns.
The predicted salary is between 43200 - 72000 £ per year.
Responsibilities:
- 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.
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.
Senior Applied Scientist employer: Microsoft
Contact Detail:
Microsoft Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Applied Scientist
✨Tip Number 1
Make sure to showcase your experience with generative models, especially diffusion and auto-regressive models. Highlight any specific projects or research you've conducted that demonstrate your expertise in image and video applications.
✨Tip Number 2
Emphasise your proficiency in optimising neural networks for NPUs. If you have worked on any projects that involved hardware accelerators, be ready to discuss the techniques you used and the results you achieved.
✨Tip Number 3
Networking is key! Connect with professionals in the AI/ML community, especially those who have experience at Microsoft. Attend relevant conferences or webinars where you can meet potential colleagues and learn more about the company culture.
✨Tip Number 4
Prepare to discuss your contributions to the scientific community. If you've published research in top-tier conferences, be ready to talk about your findings and how they relate to the role you're applying for at StudySmarter.
We think you need these skills to ace Senior Applied Scientist
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with generative models, particularly diffusion and auto-regressive models. Emphasise any work you've done with neural network optimisation for NPUs, as this is crucial for the role.
Craft a Strong Cover Letter: In your cover letter, discuss your passion for AI and your experience in developing real-world generative AI solutions. Mention specific projects or research that align with the responsibilities of the Senior Applied Scientist position.
Showcase Your Research Publications: If you have published research in top-tier conferences or journals, make sure to include this in your application. Highlight any impactful contributions you've made to the scientific community, especially in AI/ML.
Highlight Collaboration Skills: Since the role involves working with cross-functional teams, provide examples of past collaborations. Discuss how you mentored junior scientists or interns, showcasing your ability to foster a collaborative environment.
How to prepare for a job interview at Microsoft
✨Showcase Your Technical Expertise
Be prepared to discuss your experience with generative models, particularly diffusion and auto-regressive models. Highlight specific projects where you've optimised neural networks for NPUs, as this will demonstrate your technical proficiency.
✨Prepare for Collaborative Scenarios
Since the role involves working with cross-functional teams, think of examples where you've successfully collaborated with researchers, engineers, or product teams. Be ready to explain how you integrated technologies into products and the impact it had.
✨Demonstrate Your Research Contributions
If you've published research in top-tier conferences, be sure to mention this during the interview. Discuss the significance of your work and how it contributes to the scientific community, as this aligns with the company's values.
✨Mentorship Experience Matters
If you have experience mentoring junior scientists or interns, share those stories. Highlight how you fostered a collaborative environment and helped others grow, as this is an important aspect of the role.
