below marketmost similar roles pay hereabove market
This role pays more than
88%
of similar roles. Most pay
$223,625–$288,150
— the blue band above.
At the midpoint, this role pays about
$404k
versus about
$256k
for comparable roles.
Based on 240 similar postings.
Employer
About Apple Inc
Apple Inc. is a multinational technology company known for designing and manufacturing consumer electronics, software, and online services, including the iPhone, Mac, iPad, and App Store. Industry: Consumer Electronics & Software
Apple Inc currently has
3497 open roles
on FindRole.
Listed pay typically runs
$174,149–$277,600
across 2702 roles with salary data.
Director of Algorithms, Ads Engineering leads a multidisciplinary organization of engineering managers, applied scientists, ML engineers, and systems engineers within the Ads Engineering team. This leader defines the strategic vision and technical roadmap for the entire algorithms funnel, building and scaling state-of-the-art retrieval, ranking, auction, and budget optimization systems. The role involves translating research innovation into production-scale capabilities that optimize advertiser outcomes and user relevance while navigating strict privacy constraints. Key technical requirements include expertise in signals processing, candidate matching, predictive modeling for CTR/CVR, and ranking. The candidate must demonstrate proficiency with embeddings, transformer architectures, distillation methods, and reinforcement learning. This position solves complex algorithmic problems to power ads and sponsorships across various services, ensuring high-throughput, low-latency performance in a production environment.
Define the strategic vision and technical roadmap for the entire algorithms funnel.
Lead the development of retrieval, ranking, matching, auction, and budget optimization models.
Translate state-of-the-art research models into reliable, production-scale customer-facing capabilities.
Convert business objectives into algorithmic systems that optimize advertiser outcomes and user relevance.
Define success metrics and evaluation strategies through rigorous, transparent experimentation.
Manage and mentor a multidisciplinary team of engineering managers and senior technical individual contributors.
Ensure all algorithmic systems comply with strict user privacy constraints and production reliability standards.
What we're looking for
15+ years of professional experience in Machine Learning, Applied Science, or Software Engineering focusing on performance advertising, search, or recommendation systems.
10+ years of engineering leadership experience managing other managers and senior/staff-level individual contributors.
Deep technical expertise in signals processing, candidate matching, predictive modeling, and ranking.
Experience with embeddings, transformer architectures, distillation methods, and reinforcement learning.
Extensive experience building, deploying, and scaling high-throughput, low-latency ML systems in production environments.
Exceptional communication and diplomatic skills to build consensus and articulate technical strategies to diverse stakeholders.
Experience with privacy-enhancing technologies (PETs) or building ML systems under strict privacy constraints (preferred).
Ph.D. or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, Statistics, or a related quantitative field (preferred).
Machine Learning
Python
Java
Reinforcement Learning
Spark
Hadoop
Quantitative Optimization
Control Theory
Distributed Frameworks
Design of Experiments
Statistics
Forecasting
Agile
Production Systems
Machine Learning
Python
Java
Reinforcement Learning
Spark
Hadoop
Optimization
Control Theory
Statistics
Distributed Frameworks
Agile
Design of Experiments
Production Systems
Forecasting
Differential Privacy
Federated Learning
On-device Processing
Tokenization
Data De-identification
Privacy-Enhancing Technologies (PETs)
AI
ML
GDPR
CCPA
Software Development
Data Governance
Model Inference Privacy
Machine Learning
Data Platforms
Large-scale Data Processing
Streaming Systems
Experimentation
Anomaly Detection
Graph Systems
Privacy-Enhancing Technologies
Model Governance
Production Reliability
Causal Measurement
AI
Agentic Systems
data-quality