NLP Research Scientist / Engineer (ACL 2020)
- Cupertino, CA
Posted: Jun 11, 2020
Role Number: 200175339
Imagine what you could do here. At Apple, great ideas have a way of becoming great products, services, and customer experiences very quickly. Combining groundbreaking machine learning and NLP techniques with next-generation hardware, our teams take user experiences to the next level. We are looking for NLP research scientists and engineers to join our teams in Santa Clara Valley, Seattle, Boston, Pittsburgh, Beijing, and Cambridge (UK).
- Expertise in various facets of ML and NLP, such as conversational dialogue systems, classification, feature engineering, information extraction, structured prediction, clustering, semi-supervised learning, topic modeling, and ranking
- Knowledge of common ML frameworks
- Strong programming skills with proven experience crafting, prototyping, and delivering advanced algorithmic solutions
- A passion for making ML methods robust and scalable
- bility to explain and present analyses and machine learning concepts to a broad technical audience
- Be able to deliver ML technologies aligned with the core values of Apple, ensuring the highest standards of quality, innovation, and respect for user privacy
- Creative, collaborative, and innovation focused
As a member of a machine learning team at Apple, you will use your deep understanding of natural language processing, machine learning, and artificial intelligence to tackle meaningful technical problems, collaborate with the most innovative product development teams in the world, and transfer your ideas into solutions in the next generation of Apple products. You will perform fundamental research by defining, designing, implementing and evaluating algorithms involving unrivaled data and objectives. You will also actively engage with the academic community by collaborating with universities, publishing and presenting your work, and attending conferences.
Education & Experience
PhD, MS, or BS in Machine Learning, Computer Science, or related fields.
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