Job Overview:
Apple’s Hardware division is seeking a Machine Learning Data Scientist to join the Video Engineering Data Analytics and Quality (DAQ) group, focusing on evaluating machine learning and multi-modal large language models (MM-LLMs). The role involves analyzing and validating computer vision, multi-modal, and large language models to ensure they meet accuracy, robustness, and usability standards, requiring a deep understanding of ML algorithms, data processing, and model optimization techniques. Responsibilities include developing performance metrics, conducting failure analysis, cleaning and curating large-scale datasets, and collaborating with cross-functional teams to integrate models into production. The ideal candidate will have a BS in a quantitative field, at least 3 years of industry experience, advanced programming skills in SQL and Python, and expertise in data wrangling and visualization tools like Tableau and AWS. Preferred qualifications include experience with multi-modal foundation models, machine learning interpretability, and strong communication skills for presenting findings to leadership.
>> View full job details on Apple’s official website.
Resume and Interview Tips:
When tailoring your resume for the Machine Learning Data Scientist position at Apple, emphasize your hands-on experience with multi-modal large language models (MM-LLMs) and computer vision, as these are central to the role. Highlight specific projects where you evaluated ML models, developed performance metrics, or conducted failure analysis, quantifying your impact where possible. Showcase your proficiency in SQL and Python, as well as any experience with data visualization tools like Tableau or AWS. If you’ve worked with models like GPT-4o, Gemini 2.5, or LLaVA, make sure to mention them explicitly, as these are preferred qualifications. Additionally, demonstrate your ability to collaborate across teams by describing projects where you worked with engineers, product managers, or other data scientists. Your resume should reflect not only technical expertise but also strong communication skills, as presenting findings to leadership is a key part of the role.
During the interview, expect to dive deep into your experience with MM-LLMs and computer vision models. Be prepared to discuss specific instances where you identified and resolved model failures, optimized performance, or developed innovative evaluation metrics. The interviewer will likely probe your technical skills, so practice explaining complex concepts like model interpretability and data wrangling in a clear, concise manner. You may also be asked to solve real-world problems related to large-scale dataset processing or model deployment, so brush up on your Python and SQL skills. Since collaboration is a key aspect of the role, be ready to share examples of how you’ve worked with cross-functional teams and communicated technical findings to non-technical stakeholders. Finally, demonstrate your curiosity and attention to detail by asking insightful questions about Apple’s current challenges in ML model evaluation and how you could contribute to solving them.