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Machine Learning Researcher, Mlr
hace 3 semanas
Play a part in the next revolution of machine learning research. Explore new methods, challenge existing metrics or protocols, develop new insightful theories that will change the way we understand machine learning. The Machine Learning Research team led by Samy Bengio, is looking for passionate researcher to investigate the next generation of Machine Learning techniques with particular attention to multi-modality, generative models, interpretability, and alignment. As a member of the MLR team, you will work on some of the most challenging scientific questions, collaborate with world-class machine learning researchers, push forward innovative research agendas and publish ground-breaking research in international conferences.
**Description**
Are you passionate about advancing the state-of-the-art and pursuing great innovation? This role requires ability to identify gaps in the research field, define a research agenda, and implement innovative ML. Machine Learning Researchers provide technical mentorship and guidance, prepare technical reports and papers for publication and conference talks. They are able to work both independently and collaboratively to help partner teams meet predefined objectives. They are responsible for delivering ML technologies aligned with the core values of Apple, ensuring the highest standards of quality, scientific rigor, innovation, and respect for user privacy.
**Minimum Qualifications**
- In-depth expertise in machine learning (ML) and deep learning (DL).
- In-depth knowledge of diffusion models and transformers technology.
- Strong publication record in relevant ML conferences (e.g., NeurIPS, ICML, ICLR,...).
- Hands-on experience working with deep learning toolkits such as Tensorflow, PyTorch, or JAX.
**Preferred Qualifications**
- Excellent presentation and writing skills.
- Ability to formulate a research problem, design, experiment and implement solutions.
- Ability to work in a collaborative environment.A passion for one of the following areas: multi-modal models, generative AI, interpretability, or alignment.
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