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Machine Learning Engineer - Job Opportunity at Apple Inc.

New York, United States
Full-time
Mid-level
Posted: May 26, 2025
On-site
USD 143,100 - 264,200 per year

Benefits

Comprehensive dental coverage providing extensive oral health support with potential for premium provider networks
Employee stock purchase plan offering discounted Apple shares with potential for significant long-term wealth building
Premium health insurance coverage with comprehensive medical benefits and likely minimal employee contribution
Restricted Stock Units (RSU) providing equity compensation tied to company performance and vesting schedules
Robust retirement plan with potential employer matching and diverse investment options
Educational expense reimbursement supporting career advancement and skill development
Discounted Apple products and free services providing substantial value given premium product pricing
Discretionary bonus and commission opportunities based on performance metrics

Key Responsibilities

Drive end-to-end development and deployment of machine learning solutions that directly impact Apple Pay security infrastructure serving millions of users globally
Lead cross-functional collaboration with engineering teams, security specialists, and business stakeholders to translate complex security challenges into scalable analytical solutions
Own complete responsibility for transforming customer security needs into production-ready ML systems including feature engineering, model training, and performance reporting
Pioneer innovative approaches to fraud prevention and anomaly detection that push beyond traditional machine learning boundaries while maintaining Apple's privacy standards
Develop and implement predictive modeling solutions that enhance operational efficiency across Apple's vast ecosystem while handling adversarial fraud scenarios
Communicate complex analytical results and recommendations to executive leadership and business partners with clear impact assessment
Take ownership of critical problem areas within Apple Pay security, applying cutting-edge machine learning expertise in a fast-paced, innovation-driven environment

Requirements

Education

Bachelor's degree in a quantitative field such as Computer Science, Statistics, Machine learning or a related technical field or equivalent professional experience

Experience

Professional experience with machine learning algorithms and large-scale distributed systems

Required Skills

Experience with machine learning algorithms including classification, clustering algorithms and anomaly detection Understanding of relational databases, including SQL, and large-scale distributed systems such as Hadoop and Spark Strong programming skills in one or more following language: Scala, Python, Java, Objective-C or similar Efficiently and succinctly communicate results of analysis to business partners and executives Bring innovation to machine learning, data science and engineering solutions and to push beyond current tools and approaches Highly motivated and organized, with the ability to accept ambiguity and deliver outstanding results on tight schedules through multi-functional collaboration Familiarity with iOS/Swift Understanding of privacy-preserving techniques
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Sauge AI Market Intelligence

Industry Trends

The fintech security sector is experiencing unprecedented growth with machine learning-driven fraud detection becoming essential as digital payment volumes surge globally, particularly following the acceleration of contactless payments during the pandemic era. Companies are investing heavily in real-time anomaly detection systems that can adapt to evolving fraud patterns while maintaining user experience standards. Privacy-preserving machine learning techniques are becoming critical differentiators in the tech industry, with techniques like federated learning, differential privacy, and homomorphic encryption gaining prominence as regulatory frameworks like GDPR and CCPA reshape data handling requirements. Apple's emphasis on privacy-first ML aligns with this industry shift toward privacy-centric computing. The integration of mobile security with artificial intelligence is creating new paradigms in digital identity protection, where smartphones are becoming comprehensive security hubs. This trend is driving demand for ML engineers who can work at the intersection of mobile technology, security, and user experience design.

Salary Evaluation

The offered salary range of $143,100 - $264,200 is highly competitive and aligns well with senior ML engineer compensation in major tech companies. For New York market specifically, this range positions in the 75th-90th percentile for similar roles, reflecting Apple's premium positioning and the specialized nature of security-focused ML work. The significant range suggests room for negotiation based on experience level and demonstrates Apple's commitment to retaining top talent in this critical area.

Role Significance

Based on Apple's organizational structure and the cross-functional nature described, this role likely involves working within a core ML team of 8-12 engineers while collaborating with broader security and product teams totaling 50+ professionals. The role suggests matrix-style collaboration across multiple engineering disciplines.
This role represents a mid-to-senior level position with significant autonomy and cross-functional leadership responsibilities. The emphasis on 'end-to-end ownership' and 'communicating to executives' indicates this is not an entry-level position but rather one where the engineer is expected to drive strategic initiatives and influence product direction within Apple's security ecosystem.

Key Projects

Development of real-time fraud detection systems capable of processing millions of Apple Pay transactions with sub-second response times while maintaining privacy standards Implementation of federated learning systems that improve security models without centralizing sensitive user data, aligning with Apple's privacy philosophy Creation of adaptive anomaly detection frameworks that evolve with emerging fraud patterns while minimizing false positives that could impact user experience

Success Factors

Deep technical expertise in both machine learning fundamentals and their practical application to security challenges, particularly the ability to balance model accuracy with computational efficiency in real-time systems Strong collaborative and communication skills to effectively work across Apple's diverse engineering culture, translating complex technical concepts for both technical and business stakeholders Adaptability and innovation mindset to push beyond existing ML approaches, given Apple's emphasis on pioneering new solutions rather than implementing standard practices Understanding of privacy-by-design principles and their implementation in ML systems, which is crucial for Apple's brand and regulatory compliance Ability to work effectively under ambiguity and tight deadlines while maintaining high quality standards, essential in Apple's fast-paced product development environment

Market Demand

Extremely high demand exists for ML engineers with security and fraud detection expertise, particularly those with experience in privacy-preserving techniques and large-scale systems. The intersection of machine learning, mobile security, and payments represents one of the fastest-growing segments in tech hiring, with supply significantly lagging behind demand.

Important Skills

Critical Skills

Machine learning algorithm expertise is fundamental as the role requires implementing and optimizing various ML approaches for security applications, with deep understanding needed to adapt algorithms for real-time, privacy-preserving environments Programming proficiency in languages like Python, Scala, or Java is essential for implementing production ML systems that can scale to Apple's massive user base while integrating with existing infrastructure Large-scale distributed systems knowledge is crucial for handling Apple Pay's transaction volumes and implementing ML solutions that can operate efficiently across Apple's global infrastructure Communication skills are vital for cross-functional collaboration and executive reporting, as ML insights must be translated into actionable business and security strategies

Beneficial Skills

iOS and Swift familiarity provides significant advantage for mobile-first ML implementations and understanding Apple's development ecosystem Privacy-preserving ML techniques knowledge aligns with Apple's core differentiator and represents a growing area of competitive advantage Security domain expertise helps in understanding adversarial scenarios and developing robust ML solutions that can withstand sophisticated attack patterns Product development experience aids in translating ML capabilities into user-facing features that enhance rather than complicate the customer experience

Unique Aspects

Direct impact on security infrastructure used by hundreds of millions of Apple Pay users worldwide, providing immediate real-world validation of ML model effectiveness
Opportunity to work with Apple's proprietary privacy-preserving ML techniques and contribute to industry-leading approaches to secure computing
Access to unique datasets and computational resources that few other companies can provide, enabling cutting-edge research in adversarial ML and fraud detection
Integration with Apple's broader ecosystem strategy, allowing ML solutions to leverage hardware-software integration advantages unavailable to purely software-focused companies

Career Growth

Progression to senior technical roles typically occurs within 2-3 years with strong performance, while transition to management or principal engineer roles generally requires 3-5 years of demonstrated impact and leadership within Apple's ecosystem.

Potential Next Roles

Senior Machine Learning Engineer or Principal ML Engineer roles with increased technical leadership responsibilities and architectural decision-making authority Machine Learning Manager positions overseeing ML engineering teams and strategy development for Apple's security initiatives Product Manager roles focusing on AI/ML security products, leveraging technical expertise to drive product strategy Technical Director or Engineering Manager roles within Apple's broader security organization, potentially leading multiple ML teams

Company Overview

Apple Inc.

Apple Inc. stands as one of the world's most valuable technology companies, known for its integrated ecosystem of hardware, software, and services. The company's approach to machine learning is distinguished by its focus on on-device processing and privacy preservation, setting industry standards for responsible AI development. Apple's ML initiatives span across all product lines, from Siri and computer vision to the financial services offered through Apple Pay.

Apple maintains a dominant position in the premium consumer technology market and is increasingly influential in enterprise and financial services sectors. The company's ML capabilities are considered among the most advanced globally, particularly in areas requiring privacy-preserving techniques and edge computing optimization.
Apple's New York presence represents a strategic expansion beyond its California headquarters, with significant investment in East Coast talent acquisition and proximity to financial services partnerships. The New York office serves as a crucial hub for fintech initiatives and business development activities.
Apple's engineering culture emphasizes innovation, attention to detail, and cross-functional collaboration. The company is known for its high performance standards, secretive project environment, and strong emphasis on product quality over rapid iteration. Work-life balance varies by team, but the company generally offers excellent benefits and career development opportunities.
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