Apple Ads helps developers and businesses reach customers on the App Store through highly relevant, privacy-focused advertising. Our engineering teams build the infrastructure that powers ad delivery at Apple scale - from search and retrieval to auction, ranking, and measurement. Description

This platform operates at massive scale, handling tens of thousands of requests per second with strict latency requirements, and indexes millions of advertiser campaigns with complex targeting signals. We are investing heavily in evolving this platform: transitioning from batch-based architectures to streaming-first, near-real-time indexing, building modular retrieval infrastructure that scales efficiently across multiple ad placements, and introducing on-device ML scoring at retrieval time.

Develop streaming index pipelines that reflect advertiser campaign changes in near-real-time, replacing legacy batch-only workflows

Implement ad eligibility and filtering logic at retrieval time - including keyword matching, audience targeting, budget enforcement, frequency capping, and policy compliance

Drive platform generalization - abstracting core retrieval capabilities (matching, eligibility, scoring, ranking) into a shared base stack that new ad placements can build on without rebuilding from scratch

Build auto-scaling infrastructure to eliminate manual capacity operations and support organic demand growth





Contribute to offline experimentation infrastructure that allows ML teams to evaluate current ranking and matching models without risk to production traffic

Participate in on-call rotations and lead incident response for production search and indexing systems Preferred Qualifications Experience with embedding-based retrieval (EBR) or approximate nearest neighbor (ANN) search systems

Experience deploying ML models in latency-sensitive inference environments, including feature serving and model lifecycle management

Familiarity with A/B experimentation platforms and offline evaluation pipelines for ranking and retrieval

Experience designing and migrating systems from batch-only to streaming-first architectures

Track record of improving developer tooling and code quality in large, collaborative codebases - including test coverage automation, schema management, and CI/CD pipeline improvements Minimum Qualifications 3+ years of software engineering experience with a focus on backend or distributed systems

Strong programming skills in one or more of: Rust, C++, Java, or Scala

Deep understanding of search and information retrieval fundamentals - inverted indexes, posting lists, forward/reverse index construction, tokenization, query parsing, and sharding

Hands-on experience building and operating high-throughput, low-latency distributed services with tight SLA requirements

Experience with streaming data infrastructure such as Apache Kafka, Amazon Kinesis, or equivalent systems

Familiarity with container orchestration (e.g., Kubernetes) and operating services in large-scale cloud environments

📌 Software Engineer - Search & Indexing Platform, Apple Ads (Hyderabad)
🏢 Apple
📍 Hyderabad