Apple's Camera & Photos Tools & AI team is a tight-knit engineering team building the internal tools that power how the Camera, Photos, and Image Quality teams measure, evaluate, and improve the imaging experience on Apple products. Our software sits at the center of some of Apple's most demanding imaging workflows: it captures and catalogs enormous volumes of images and videos, orchestrates long-running analyses that characterize camera performance, and surfaces the results to the engineers and scientists who tune the hardware and software behind every photo our customers take. We move quickly, care deeply about the craft, and thrive on turning ambiguous problems into reliable, well-designed tools. As a member of this team, you will ship software across the full stack, from native Swift applications and modern web frontends to Python service backends, and you will partner with a wide range of engineering, science, and quality teams to understand their workflows and build what they need. As AI capabilities advance rapidly, our team is actively building AI-native tooling, from integrating multimodal and vision models into image quality workflows to designing LLM- powered interfaces that let engineers query and interpret large datasets in natural language. We're looking for someone who doesn't just use AI as a productivity aid, but who thinks critically about where and how to embed it into reliable, maintainable engineering systems. If you enjoy owning problems end-to-end, writing code that people rely on, and collaborating with partners across multiple disciplines, we would love to talk to you. Description We are seeking a versatile and technically strong Software Engineer to help design, build, and own end-to-end development of the internal tooling that supports imaging engineering and quality workflows across Camera, Photos, and Image Quality. You will contribute to multiple Swift applications, React-based web frontends, and Python REST API services, and leverage Apple infrastructure to run asynchronous compute jobs. The ideal candidate is an experienced generalist who is comfortable moving between client, web, and backend code; has a solid grasp of distributed-systems fundamentals; and writes code with an eye toward maintainability, correctness, and long-term operability. You are equally at home designing a new service, debugging a tricky async job, polishing a UI workflow, and sitting down with a partner team to understand what they actually need before writing a line of code. You bring informed opinions about where AI genuinely improves a system, and where it adds unnecessary complexity, and you hold AI-powered features to the same engineering standards as any other production code. Above all, you are a strong communicator who treats cross-functional collaboration as a core part of the job. Minimum Qualifications BS in Computer Science, Computer Engineering, or equivalent experience. 4+ years of professional software engineering experience shipping production software. Proficiency in at least two of: Swift, Python, and JavaScript/TypeScript, with a track record of contributing meaningfully in both client and server code. Strong understanding of REST API design and experience building production REST services. Experience building web frontends with React or native macOS or iOS apps in SwiftUI. Demonstrated experience integrating AI/ML models (LLMs, vision models, or similar) into production software systems, not just as a user but as a builder responsible for reliability and maintainability. Working knowledge of asynchronous job execution patterns (background workers, task queues, or similar) for long-running computations. Solid understanding of software engineering fundamentals: data modeling, API design, testing, debugging, and code review. Strong written and verbal communication skills, with a demonstrated ability to work effectively with partners outside of engineering. Preferred Qualifications Experience building production features with LLM APIs (e.g., OpenAI, Anthropic, or on- device models), including prompt design, context window management, output validation, and graceful degradation. Familiarity with multimodal or computer vision models applied to image analysis, quality assessment, or visual data retrieval, with an understanding of where these models succeed and fail in practice. Experience with vector databases or semantic search (e.g., pgvector, Pinecone, Weaviate) for unstructured or high-dimensional data retrieval pipelines. Understanding of MLOps principles: model deployment pipelines, versioning strategies, evaluation frameworks, A/B testing for AI features, and production monitoring for model quality and cost. Awareness of bias and fairness considerations in AI systems, particularly in visual domains, including diverse evaluation datasets, inclusive quality benchmarks, and responsible deployment practices. Experience developing native macOS or iOS applications in Swift, including familiarity with Xcode. Experience designing and operating distributed systems, including awareness of the tradeoffs involved in consistency, coordination, and failure handling. Familiarity with Solr (or other search platforms such as Elasticsearch) for indexing and querying large datasets. Familiarity with Redis, whether as a cache, message broker, or coordination primitive. Comfort working with image data, metadata pipelines, or scientific/engineering workflows. Exceptional cross-functional collaboration skills: stakeholder alignment, documentation, and presenting technical work to non-engineering partners. Comfortable and adaptable in a fast-paced environment with shifting priorities and multiple stakeholders.