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- Aufgaben: Join a team to optimize AI models and implement cutting-edge machine learning algorithms.
- Arbeitgeber: Be part of Amazon Web Services, a leader in cloud computing and AI innovation.
- Mitarbeitervorteile: Enjoy a collaborative work environment with opportunities for research and development.
- Warum dieser Job: Make an impact in AI while working with diverse experts and engaging with the academic community.
- Gewünschte Qualifikationen: PhD or Master's in CS, CE, ML; programming skills in Java, C++, Python; deep learning experience required.
- Andere Informationen: Located in Tübingen, Germany; EU work permit required.
Das voraussichtliche Gehalt liegt zwischen 48000 - 84000 € pro Jahr.
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Client:
Amazon Web Services Development Center Germany GmbH
Location:
Job Category:
Education
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EU work permit required:
Yes
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Job Reference:
31c911fb1ec7
Job Views:
2
Posted:
07.03.2025
Expiry Date:
21.04.2025
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Job Description:
AWS AI Research & Engineering (AIRE) is looking for scientists and engineers to work on optimizing foundation models for inference in Tuebingen, Germany. At AIRE, we actively work on applying compiler, high-performance computing, and computer architecture techniques, amongst others, to optimize the performance of foundation model execution, including training and inference. Join us to work as an integral part of a team that has diverse experiences in this space. You will invent, implement, and deploy state of the art machine learning algorithms and systems to improve the inference of foundation models. To this end, you will interact closely with our customers—product orgs—and with the academic and research communities.
BASIC QUALIFICATIONS
- PhD, or a Master’s degree and experience in CS, CE, ML or related field
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- Experience programming in Java, C++, Python or related language
- Experience with Machine Learning
- Experience with mathematical optimization, parallel and distributed computing, high-performance computing
- Solid technical understanding of modern deep learning architectures like Transformers
- Experience with deep learning frameworks like PyTorch
PREFERRED QUALIFICATIONS
- Experience with programming hardware accelerators (e.g., GPU / TPU / Neuron)
- Experience with inference optimization of foundation models (e.g., model compression techniques like distillation/pruning/sparsification/quantization, architectural optimization like mixture of experts, decoding optimization like speculative decoding/adaptive inference, system-level optimization like distributed inference/persisting KV caching/dynamic batching)
- Experience with inference engines (e.g., vLLM, TGI)
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Applied Scientist II, AI Research & Education, Tübingen Arbeitgeber: TN Germany
Kontaktperson:
TN Germany HR Team
StudySmarter Bewerbungstipps 🤫
So bekommst du den Job: Applied Scientist II, AI Research & Education, Tübingen
✨Tip Number 1
Make sure to showcase your experience with machine learning and deep learning architectures in your conversations. Highlight any specific projects where you've optimized inference or worked with foundation models, as this will resonate well with the team.
✨Tip Number 2
Familiarize yourself with the latest trends in high-performance computing and compiler techniques. Being able to discuss recent advancements or your own insights on these topics can set you apart during interviews.
✨Tip Number 3
Engage with the academic community by attending relevant conferences or workshops. Networking with professionals in the field can provide valuable insights and potentially lead to recommendations or collaborations.
✨Tip Number 4
Prepare to discuss your programming skills in languages like Java, C++, and Python. Be ready to share examples of how you've applied these skills in real-world scenarios, especially in relation to machine learning and optimization.
Diese Fähigkeiten machen dich zur top Bewerber*in für die Stelle: Applied Scientist II, AI Research & Education, Tübingen
Tipps für deine Bewerbung 🫡
Understand the Role: Make sure to thoroughly read the job description for the Applied Scientist II position. Understand the key responsibilities and qualifications required, especially in areas like machine learning, programming languages, and optimization techniques.
Highlight Relevant Experience: In your CV and cover letter, emphasize your PhD or Master's degree and any relevant experience in computer science, machine learning, or related fields. Include specific examples of your work with deep learning architectures and any publications or patents you may have.
Showcase Technical Skills: Clearly outline your programming skills in languages such as Java, C++, and Python. Mention any experience with deep learning frameworks like PyTorch and your familiarity with hardware accelerators and inference optimization techniques.
Tailor Your Application: Customize your application materials to reflect the specific requirements and preferred qualifications listed in the job description. Use keywords from the posting to demonstrate that you are a strong fit for the role.
Wie du dich auf ein Vorstellungsgespräch bei TN Germany vorbereitest
✨Showcase Your Technical Expertise
Be prepared to discuss your experience with programming languages like Java, C++, and Python. Highlight any projects where you've implemented machine learning algorithms or worked with deep learning frameworks such as PyTorch.
✨Discuss Your Research Contributions
If you have patents or publications, make sure to mention them. Discuss the impact of your work in top-tier peer-reviewed conferences or journals, as this demonstrates your commitment to advancing the field.
✨Demonstrate Problem-Solving Skills
Prepare to talk about specific challenges you've faced in optimizing foundation models or working with high-performance computing. Use examples that showcase your ability to think critically and apply mathematical optimization techniques.
✨Engage with the Team's Vision
Research AWS AI Research & Engineering's current projects and goals. Be ready to discuss how your skills and experiences align with their mission to optimize model execution and improve inference performance.
