Machine Learning Scientist / Engineer (KDD 2020)
Santa Clara Valley (Cupertino), California, United States
Software and Services
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 techniques with next-generation hardware, our teams take user experiences to the next level. We are looking for machine learning scientists and engineers to join our teams in Santa Clara Valley, Seattle, Pittsburgh, Boston, Beijing, and Cambridge (UK).
- Strong background in machine learning and artificial intelligence with expertise in one or more of: computer vision, NLP, optimization, deep learning, reinforcement learning, time series, generative models, signals, and distributed systems.
- Knowledge of common ML frameworks
- A passion for making ML methods robust and scalable
- Strong programming skills with proven experience crafting, prototyping, and delivering advanced algorithmic solutions
- Ability 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 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.