Job Overview:
Apple’s Hardware department is seeking a Machine Learning/Data Scientist to join the Data Analytic and Quality (DAQ) group, focusing on the evaluation of GenAI models. This role involves collaborating with ML engineers, Data Scientists, and ML Infrastructure Engineers to develop methods for evaluating and improving foundation models, refining training data, and designing image/video analysis and quality assessment algorithms. Responsibilities include crafting testing strategies, conducting failure analysis on large GenAI models, prototyping novel evaluation methods, developing data analysis tools, and designing experiments for engineering and user studies. The ideal candidate will have a strong background in Python, ML frameworks, computer vision, and 3D deep learning techniques, with hands-on experience in generative ML models and 3D data processing.
>> View full job details on Apple’s official website.
Resume and Interview Tips:
To tailor your resume for this Machine Learning/Data Scientist role at Apple, emphasize your technical expertise in Python, TensorFlow, and PyTorch, as well as your hands-on experience with computer vision and 3D deep learning techniques like NeRF, PointNet, and Diffusion Models. Highlight any projects where you’ve worked with image/video/3D generative ML models or processed 3D data (point clouds, meshes, etc.). If you have experience with 3D data processing frameworks (Blender API, Unreal Engine, etc.) or real-time rendering, include that as well. Quantify your impact where possible, such as improvements in model performance or efficiency. Showcase your ability to collaborate with cross-functional teams and your contributions to evaluating or improving foundation models. Your resume should reflect a balance of technical skills, practical experience, and problem-solving abilities relevant to GenAI model evaluation.
During the interview, expect questions that assess your technical proficiency in Python, ML frameworks, and computer vision, as well as your experience with 3D deep learning techniques. Be prepared to discuss specific projects where you’ve worked with generative ML models or evaluated foundation models. The interviewer may ask about your approach to designing evaluation methods, analyzing model failures, or improving training data. Practice explaining complex concepts clearly and concisely, as you’ll need to demonstrate your ability to collaborate with diverse teams. Be ready to tackle coding challenges or whiteboard problems related to computer vision and 3D data processing. Show enthusiasm for solving cutting-edge problems in GenAI and highlight your ability to think critically about model evaluation and improvement. Dress professionally but comfortably, as the focus will be on your technical expertise and problem-solving skills.