ML Data Scientist - Job Opportunity at Apple
USD 100,000 - 150,000 per year based on Hong Kong market rates for ML specialists at major tech companies
Benefits
Access to cutting-edge ML/AI technology stack
Career development in a leading tech company
Work on high-impact projects affecting millions of users
Collaboration with world-class technical teams
Professional development opportunities in emerging AI fields
Key Responsibilities
Design and implement evaluation workflows for ML models to ensure quality and reliability
Conduct comprehensive failure analysis and develop improvement strategies
Lead data curation initiatives and establish quality guidelines
Drive cross-functional collaboration for model evaluation and enhancement
Develop automated tools for model performance monitoring
Guide data annotation teams and establish quality protocols
Requirements
Education
Bachelor's/Master's/PhD degree in Computer Science, Data Science, Artificial Intelligence, or related disciplines
Experience
Open to both recent graduates and professionals with industry experience
Required Skills
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Sauge AI Market Intelligence
Industry Trends
Role Significance
Likely part of a specialized 5-8 person team within larger ML organization
Mid to Senior level position with significant impact on product quality and risk management
Key Projects
Success Factors
Market Demand
Very High - Specialized role combining ML evaluation and quality assurance is increasingly critical as AI deployment scales
Important Skills
Critical Skills
Beneficial Skills
Unique Aspects
Focus on ML model transparency and quality at massive scale
Opportunity to impact millions of users through AI quality assurance
Combination of technical depth and quality assurance expertise
Position at intersection of ML engineering and product quality
Career Growth
2-3 years in role before advancement opportunities, depending on impact and skill development
Potential Next Roles
Company Overview
Apple
Apple is a global technology leader with significant investments in AI/ML capabilities across its product ecosystem
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Data Sources & Analysis Information
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