AIML - Sr Backend Engineer, Data and ML Innovation

Are you excited to tackle some of the most ambitious technical challenges in Apple Intelligence? Be involved in collaborating closely with our machine learning researchers, engineers, and data scientists? Together, you will orchestrate groundbreaking research initiatives and develop transformative products crafted to build a significant impact for billions of users worldwide! As part of Apple's AI and Machine Learning org, we encourage and build groundbreaking technology for large-scale ML systems, computer vision, natural language processing, and multi-modal understanding and generation. The Data and Machine Learning Innovation (DMLI) team is looking for Sr Backend Engineer to build world-class data solutions that are used by data scientists, ML engineers and researchers to power Apple Foundation Model lifecycle.

We are looking for a skilled Python Backend Engineer to join our development team. In this role, you will be responsible for building and maintaining the server-side logic. You will be tasked with developing back-end components, connecting the application with third-party web services, and supporting the front-end developers by integrating their work with the Python application. You have demonstrated expertise in Python. Knowledge of Data Engineering & ML domain & training lifecycle is a plus. You are comfortable with analyzing business requirements, identifying gaps, and translating requirements into technical designs. You are proficient and adhere to the best practices of software development, such as agile development, code reviews, continuous integration, and automated testing. You have worked closely with project managers, UI/UX designers, and other cross functional stakeholders to deliver high quality work timely.

• Bachelor's degree in Computer Science or related field.
• 3+ years of extensive backend development experience.
• Expertise in architecting and developing backend systems using Python; extensive experience in FastAPI, Django, or Flask will be great.
• In-depth knowledge of workflow orchestration systems like Airflow, distributed systems, cloud-native applications, and containerization technologies like Docker and Kubernetes.

• Experience in Data Engineering & ML domain & training lifecycle.
• Experience with large-scale data processing, distributed systems , microservices, various caching strategies, and performance optimization.
• Excellent problem solving skills and be able to navigate through complex technical challenges and design decisions.
• Experience with cloud platforms like AWS or GCP.
• Master or Ph.D. in a related field.
• Previous experience in a high-growth tech company or similar environment.