Sr. ML Engineer, Indexing Infrastructure
Listed on 2026-11-05
-
Software Development
Machine Learning/ ML Engineer, AI Engineer (Applied/Software), Cloud Engineer - Software, Software Engineer
Santa Clara, California, United States Machine Learning and AI
Imagine what you could do here. At Apple, great ideas have a way of becoming great products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish.
Do you want to make Siri and Apple products smarter for our users? The MLPT & Infra teams are building groundbreaking technology for algorithmic search, machine learning, natural language processing, and artificial intelligence. Our universal search engine powers search features across a variety of Apple products, including Siri, Spotlight, Safari, Messages and Lookup.
As part of this group, you will build the storage and compute foundation behind one of the most exciting high-performance computing environments: trillions of records, petabytes of data, and the systems that turn them into search and Apple Intelligence experiences that delight our customers every day.
Responsibilities- Design and build our web signal store, a petabyte-scale Parquet/Iceberg lakehouse holding trillions of web documents and signals.
- Design and build our in-house Map Reduce-style processing framework, which runs thousands of workers on Kubernetes.
- Improve performance, reliability, and cost at trillion-row scale, from read/write throughput and compaction to fault-tolerant batch jobs on shared clusters.
- Own production health, including monitoring, on-call, and root-cause fixes, and raise the engineering bar through design and code reviews.
- Strong coding skills and a solid computer science background in algorithms, data structures, and distributed systems
- 5+ years experience designing, building, and operating large-scale distributed systems such as databases, storage platforms, or data processing frameworks
- Hands-on experience with petabyte-scale batch data processing (Map Reduce, Spark, or similar)
- Proficiency in cloud-native architectures and compute orchestration on AWS (S3, EC2, EKS) and Kubernetes
- Excellent interpersonal skills, with the ability to work independently and drive cross-team efforts
- MS or PhD in Computer Science or related field
- Deep familiarity with columnar and lakehouse formats (Parquet, Iceberg), and the judgment to choose between them at scale
- Experience with storage internals: compaction, metadata and manifest management, indexing, and query performance tuning
- Experience with workflow orchestration (e.g., Argo) and resource scheduling on shared Kubernetes clusters
- Experience with key-value or online serving stores (e.g., Bigtable, HBase, Cassandra) and multi-cloud data movement
- Experience with search indexing, machine learning, or NLP is a plus
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $216,200 and $324,800, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan. You’ll also receive benefits including:
Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses — including tuition. Additionally, this role might be eligible for discretionary bonuses or…
(If this job is in fact in your jurisdiction, then you may be using a Proxy or VPN to access this site, and to progress further, you should change your connectivity to another mobile device or PC).