Director of Algorithms, Ads Engineering

Apple Apple · Big Tech · Cupertino, CA · Software and Services

Director of Algorithms for Apple Ads, leading applied scientists and ML engineers to build and scale intelligence for ads delivery. Focuses on retrieval, ranking, auction, and budget optimization systems at massive scale, balancing research innovation with operational excellence and privacy constraints. This role involves defining strategy, roadmap, and execution for ML systems that optimize advertiser outcomes and user relevance.

What you'd actually do

  1. Define the strategic vision and technical roadmap for the entire algorithms funnel, delivering intelligence across all phases of ads delivery -- including retrieval, ranking, matching, auction, and budget optimization models.
  2. Champion algorithm engineering excellence end-to-end across a complex production ecosystem, partnering with infrastructure, serving, privacy, and experimentation teams to translate state-of-the-art models into reliable, customer-facing capabilities.
  3. Drive clear execution plans across organizational boundaries, balancing research innovation with operational excellence, launch readiness, and production reliability.
  4. Partner deeply with Ads Product leaders to co-drive marketplace strategy and translate business objectives into algorithmic systems that optimize advertiser outcomes, user relevance, auction efficiency, and long-term marketplace health.
  5. Partner closely with Data Insights and experimentation teams to define success metrics, measurement frameworks, and evaluation strategies that ground algorithmic decisions in rigorous, transparent experimentation.

Skills

Required

  • Machine Learning
  • Applied Science
  • Software Engineering
  • performance advertising, search, or recommendation systems
  • engineering leadership
  • managing other managers
  • senior/staff-level individual contributors
  • signals processing
  • candidate matching
  • predictive modeling (e.g., CTR/CVR)
  • ranking
  • embeddings
  • transformer architectures
  • distillation methods
  • reinforcement learning based methods
  • algorithm engineering
  • build, deploy, and scale high-throughput, low-latency ML systems in production environments
  • Exceptional communication and diplomatic skills
  • build consensus
  • navigate complex cross-functional relationships
  • articulate technical strategies to both technical and non-technical stakeholders
  • fostering an inclusive, respectful, and highly collaborative team culture
  • user privacy
  • delivering premium user experiences

Nice to have

  • privacy-enhancing technologies (PETs)
  • building ML systems under strict privacy constraints
  • Ads technology and domain
  • Ph.D. or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, Statistics, or a related quantitative field

What the JD emphasized

  • performance advertising, search, or recommendation systems
  • managing other managers
  • Exceptional communication and diplomatic skills
  • user privacy

Other signals

  • state-of-the-art retrieval, ranking, auction, and budget optimization systems
  • massive scale
  • privacy constraints
  • production-scale engineering
  • customer-facing capabilities
  • optimize advertiser outcomes, user relevance, auction efficiency, and long-term marketplace health