Head of Evaluation & ML Quality

Apple, Inc.; Cupertino (CA); Sur place; USD 180 000 - 260 000; Plein temps

Résumé du poste

Apple, Inc. is seeking a hands-on Senior Manager of Evaluation for Services Special Projects in Cupertino, CA.

You will build and lead a team of applied scientists and ML engineers to measure the quality of a multi-year Apple initiative, establishing when the system is correct, relevant, and trustworthy before and after release. You will own evaluation components, including methods, datasets, and automated grading systems, and drive alignment with engineering and executive leadership.

Qualifications

• 6+ years of experience in machine learning, data science, or machine learning evaluation.
• 3+ years of direct people management of scientists or engineers.
• Track record of hiring, developing, and leading teams of applied scientists or ML engineers.
• Hands-on experience building and deploying LLM-based autograders and rubrics.
• Experience evaluating conversational AI, search, or ranking systems with offline and online metrics.
• Strong statistics knowledge and experimental design, incl. A/B testing.
• Excellent written and verbal communication with senior engineers and leaders.

Responsabilités

• Lead a team that designs and runs evaluation for a conversational experience.
• Own evaluation methods, datasets, and automated grading systems.
• Set technical direction for LLM-based autograders and rubrics.
• Partner with engineering and ML teams to diagnose failures and ship fixes.
• Grow senior contributors and recruit top talent.

Connaissances

Machine learning

Data science

Team leadership

Applied ML evaluation

Statistics

A/B testing

Communication skills

Formation

MS or PhD in Computer Science, ML, Statistics, or related field

Description du poste

Apple, Inc. is seeking a hands-on Senior Manager of Evaluation for Services Special Projects in Cupertino, CA.

You will build and lead a team of applied scientists and ML engineers to measure the quality of a multi-year Apple initiative, establishing when the system is correct, relevant, and trustworthy before and after release. You will own evaluation components, including methods, datasets, and automated grading systems, and drive alignment with engineering and executive leadership.