Senior Software Engineer

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

The people here at Apple don't just create products - they create the kind of wonder that's revolutionized entire industries. It's the diversity of those people and their ideas that inspires the innovation that runs through everything we do, from amazing technology to industry-leading environmental efforts. Join Apple, and help us leave the world better than we found it.

Description

Apple's Manufacturing Systems and Infrastructure (MSI) team is responsible for capturing, consolidating, and tracking manufacturing data for all Apple products and modules globally. Our tools empower teams across Apple to confidently use data to shape the future of product manufacturing. We are seeking a Senior Software Engineer with a strong aptitude for data engineering to join our multi-functional team. In this role, you will bridge the gap between user-facing applications and high-scale data infrastructure. You will design, build, and scale critical mission-critical microservices, web applications, and data pipelines that power Apple's global factory operations.

Minimum Qualifications

7+ years of hands-on experience in software engineering, designing, and building scalable full-stack applications

Backend Engineering: Proficiency in at least one modern backend language such as Golang, Java, Python, or Rust

Frontend Development: Experience with modern web technologies, specifically React.js (Redux/Context API),TypeScript

GenAI Proficiency: Experience utilizing Generative AI tools (e.g., Claude, GitHub Copilot,

ChatGPT) to accelerate development cycles, refactor code, generating unit tests, and troubleshoot complex technical issues

Data proficiency: Hands-on experience with SQL and NoSQL databases (Postgres, MongoDB, Cassandra, foundationDB) and data modeling concepts

Streaming & Distributed Systems: Familiarity with message queues and event-driven architectures (e.g., Kafka, RabbitMQ) and distributed system concepts (consistency, availability ,partitioning)

Preferred Qualifications

ML: ML pipeline orchestration (Airflow, Kubeflow, MLflow, or similar), Strong Python; traditional ML frameworks (scikit-learn, XGBoost, LightGBM, time-series) Knowledge. CI/CD for ML; monitoring and observability frameworks

Infrastructure & Cloud: Knowledge with containerization and orchestration (Docker, Kubernetes) and public cloud platforms (AWS, Google Cloud Platform , or Azure), networking, storage

DevOps: Knowledge of CI/CD pipelines, GitOps workflows, and monitoring tools (Prometheus, Grafana)

Soft Skills: Strong analytical thinking, problem-solving skills, and the ability to collaborate effectively with cross-functional partners.