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Apple Machine Learning Engineer China, Beijing, Beijing 445008179
18.05.2024
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Key Qualifications
- Demonstrated expertise in machine learning, deep learning, or reinforcement learning, with a preference for practical experience in at least one of the following domains: time series forecasting, anomaly detection, search and recommendation systems, natural language processing, convex optimization, graph mining, or computer vision.
- Proficiency in implementing data-intensive pipelines and applications using programming languages such as Python, Java or C++.
- Working knowledge of large-scale distributed systems, with a preference for hands-on experience in distributed training and real-time inference.
- Excellent verbal and written communication skills in both Mandarin and English, capable of conveying rigorous technical concepts to non-experts.
- Ability to extract valuable business insights from data and uncover the underlying narratives behind patterns.
Description
• Collaborate with stakeholders to identify opportunities, grasp requirements, and translate them into effective technical solutions.• Formulate machine learning solutions, leveraging established methods or crafting custom algorithms to address specific business challenges.• Work closely with data engineers and platform architects to implement robust real-time and batch decisioning solutions.• Communicate insights and findings to business partners or executives.• Research and evaluate emerging technologies and methodologies in machine learning, data science, data engineering, and data visualization to enhance the team’s technical capabilities.• Monitor operational and business metrics, detect adverse trends, recognize behavioral patterns, and respond with agile logic adjustments
Education & Experience
• Ph.D. in Computer Science, Machine Learning, Statistics, Operations Research, Mathematics, Engineering, or a related field; alternatively, • M.S. in a related field with a minimum of 3 years’ experience applying machine learning to solve real-world business challenges.
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