Backend Software Engineer - Camera & Photos Tools & AI Team

Summary

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 about the craft, and turn ambiguous problems into reliable, well-designed systems. You'll own backend services and data infrastructure end to end, from Python REST APIs to the model-serving infrastructure behind our AI-native tooling, partnering with engineering, science, and quality teams across Camera, Photos, and Image Quality. 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 want someone who doesn't just call a hosted API, but who can design, deploy, and operate the serving layer underneath it, and who holds AI-powered features to the same engineering bar as any other production code.

If you enjoy owning problems end-to-end, writing services that people rely on, and collaborating across disciplines, we'd love to talk to you.

Description

We're seeking a versatile, technically strong Backend Software Engineer to design, build, and own backend infrastructure for imaging engineering and quality workflows across Camera, Photos, and Image Quality, building and operating Python REST API services, designing data models for enormous volumes of image and metadata records, and running and scaling asynchronous compute jobs, including the serving infrastructure for our AI/ML models. The ideal candidate has a solid grasp of distributed-systems fundamentals and is comfortable owning a service from API design through production operation, writing code with an eye toward maintainability, correctness, and long-term operability, and is equally at home designing a new service, debugging a tricky async job, standing up model-serving infrastructure, or sitting with a partner team to understand what they actually need. You hold AI-powered features to the same engineering standards as any other production code, and you treat cross-functional communication as a core part of the job.

Responsibilities

• Design, deploy, and operate model-serving infrastructure for LLM integrations, agentic workflows, and vision pipelines, including hosting, versioning, monitoring, and cost/latency/accuracy tradeoffs across the service lifecycle, not just calling hosted third-party APIs.
• Develop prompt engineering strategies and retrieval-augmented systems (RAG) and the underlying vector storage/retrieval infrastructure that make internal image and metadata corpora accessible and actionable to partner teams.
• 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 and maintain Python REST API backends and data models/storage for large-scale image, video, and metadata catalogs, including endpoints that kick off, monitor, and scale long-running asynchronous jobs.
• Partner with engineers, scientists, and quality leads across Camera, Photos, and Image Quality to translate their workflows into reliable backend services.
• Drive the reliability, performance, and observability of services other teams depend on; contribute to technical design, code review, and cross-team planning.

Minimum Qualifications

• BS in Computer Science, Computer Engineering, or equivalent experience.
• 4+ years of professional software engineering experience shipping production backend systems.
• Strong proficiency in Python, with a track record of owning production backend services end to end.
• Strong understanding of REST API design and experience building and operating production REST services at scale.
• Demonstrated experience hosting and serving AI/ML models (LLMs, vision models, or similar) in production, including infrastructure for inference, scaling, and monitoring, not just integrating third-party hosted APIs.
• Working knowledge of asynchronous job execution patterns (background workers, task queues, or similar) for long-running computations, and experience scaling these systems under load.
• Solid understanding of distributed-systems fundamentals: consistency, coordination, failure handling, and tradeoffs between them.
• 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

• Hands-on experience with specific self-hosted GPU inference frameworks (e.g., vLLM, Triton, Ray Serve, or similar) at production scale, beyond the general hosting/serving experience required above.
• 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.
• Hands-on operational experience with container orchestration (e.g., Kubernetes) and infrastructure-as-code for distributed systems, beyond the conceptual fundamentals required above.
• 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.
• Comfortable and adaptable in a fast-paced environment with shifting priorities and multiple stakeholders.