Full Stack Software Engineer - Camera & Photos Tools & AI Team

Design and build AI-powered features within internal tools, including LLM integrations, agentic workflows, and vision model pipelines for automated image quality analysis.

Evaluate, integrate, and maintain AI/ML models in production: monitoring for quality regression, managing model versions, and balancing cost, latency, and accuracy tradeoffs across the service lifecycle.

Develop prompt engineering strategies and retrieval-augmented systems (RAG) that make internal image and metadata corpora accessible and actionable to partner teams.

Leverage AI coding assistants and productivity tooling to accelerate development cycles and raise overall team velocity.

Plan, design, implement, and own Swift applications used by imaging engineers and quality teams.

Build and evolve React/JavaScript web frontends that surface data, tooling, and workflows to a broad internal audience.

Develop and maintain Python REST API backends, including endpoints that kick off and monitor long-running asynchronous jobs.

Partner with engineers, scientists, and quality leads across Camera, Photos, and Image Quality to understand their workflows and translate them into reliable tools.

Drive the reliability, performance, and observability of services that other teams depend on daily.

Contribute to technical design discussions, code review, and cross-team planning; raise the bar for the engineers around you.

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., 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, communicator who treats cross-functional collaboration as a core part of the job., 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.

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 $150,400 and $277,600, 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 commission payments as well as relocation. Learn more about Apple Benefits

Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

At Apple, new ideas have a way of becoming extraordinary products and experiences very quickly. Bring your passion and dedication to your job and there's no telling what you could accomplish. 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.