Application Deadline: 5th December 2025
Do you want to make Siri and Apple products smarter for our users? Here in the Machine Learning Platform Technology group we build groundbreaking technology for algorithmic search, machine learning, natural language processing, and artificial intelligence. The features we build are redefining how hundreds of millions of people use their computers and mobile devices to search and find what they are looking for. Siri’s universal search engine powers search features across a variety of Apple products, including Siri, Spotlight, Safari, Messages, Lookup, and more. As part of this group, you will work with one of the most exciting high performance computing environments on Apple’s search product, with petabytes of data, millions of queries per second, and have an opportunity to imagine and build products that delight our customers every single day.
Description
In this role working on eval infrastructure for search you will be helping with empowering engineers working on relevance with their experimentation needs, letting them iterate more quickly on their ideas within the infrastructure that serves our large scale indexes. For example, you would help them by designing end-to-end solutions that allow them to get insights into the impact their work have on the search quality, or enable them to evaluate with confidence the changes they make.
Minimum Qualifications
Bachelor's or Master’s degree in Computer Science/Engineering, or equivalent experience.
Experience with at least one of the following programming languages: Go, Java, Python, Scala, C/C++, Rust
Strong background in computer science: algorithms and data structures
Phenomenal interpersonal skills is required; able to work independently as well as in a team
Preferred Qualifications
Exposure to distributed computing platform and technologies such as AWS, GCP , Kubernetes, MapReduce, or similar
Exposure to the challenges of scalable backend infrastructure and performance and how to diagnose, analyse, and resolve them with knowledge of profiling, debugging, tracing tools
Exposure to designing and implementing large scale data pipelines
Responsibilities
Designing and developing solutions to enable and orchestrate reliable data extraction and analysis at scale
Developing and integrating experimentation-focused systems that accelerate the iterations with ML models against large indexes
Building tooling that let engineers conduct opportunity analysis and identify where they can bring value most
Designing and implementing systems that integrate with our retrieval augmented generation and have insights into how these components behave
Designing features and systems that enable to perform retrieval on large token and embeddings-based indexes
Streamlining onboarding and experimentation experience to our search systems to empower other teams to more efficiently use our components and iterate faster on their relevance improvements
Eeo Content
At Apple, we’re not all the same. And that’s our greatest strength. We draw on the differences in who we are, what we’ve experienced, and how we think. Because to create products that serve everyone, we believe in including everyone. Therefore, we are committed to treating all applicants fairly and equally. We will work with applicants to make any reasonable accommodations.
