AIML - Backend Engineer, Measurements

Join Apple as a Backend Engineer in the AIML Measurement team, where you'll play a crucial role in developing and maintaining systems that ensure the quality and safety of Apple Intelligence across all platforms. This position offers an exciting opportunity to work with world-class experts while pushing the boundaries of what's possible in AI and ML technology.

As a key member of the team, you'll be responsible for creating robust data pipelines and analytics tools that generate actionable insights from vast datasets. You'll implement scalable backend systems and frameworks for continuous quality improvement of Apple Intelligence, ensuring our AI/ML models meet the highest standards of excellence and reliability.

The role requires a strong background in backend engineering, with at least 2 years of experience in large-scale software system design and implementation. You should be proficient in languages such as Python, Java, GoLang, C++, or Scala, and have experience with distributed systems, databases, and cloud platforms. A Bachelor's degree in Computer Science, Engineering, or a related field is required.

Working at Apple means joining a company that values innovation and creativity. You'll be offered a competitive compensation package with a base salary range of $135,400 to $250,600, plus opportunities for stock awards and bonuses. The benefits package includes comprehensive medical and dental coverage, retirement benefits, educational expense reimbursement, and employee stock purchase programs.

You'll be working in Seattle, collaborating with cross-functional teams including data scientists, frontend engineers, and product managers. This is an excellent opportunity for someone who thrives in a fast-paced environment and wants to make a significant impact on technology that millions of people use daily. The role offers both technical challenges and growth opportunities, making it ideal for engineers who are passionate about AI/ML and want to work on cutting-edge technology at scale.