Job Overview:
As a Senior Machine Learning Engineer at Apple’s Machine Learning and AI division, you will design and build infrastructure to support foundation model training, enabling capabilities in text, image, speech, and video understanding and generation. Your work will involve large-scale systems with trillions of rows and petabytes of data, requiring strong coding skills in C++ or Python, deep expertise in machine learning frameworks like TensorFlow or PyTorch, and experience in scaling models efficiently. You’ll collaborate with teams to improve retrieval, ranking, and data quality while contributing to features that impact billions of users. A background in computer science, algorithms, and data structures is essential, along with the ability to work independently and in teams. Preferred qualifications include experience with large-scale language models and advanced degrees in related fields.
>> View full job details on Apple’s official website.
Resume and Interview Tips:
To tailor your resume for this role at Apple, focus on highlighting your hands-on experience with large-scale machine learning systems and infrastructure. Emphasize projects where you’ve worked with foundation models, whether in training, optimization, or deployment. Quantify your impact—mention the scale of data you’ve handled (e.g., trillions of rows or petabytes) and any efficiency improvements you achieved. Showcase your proficiency in C++ or Python, as well as frameworks like TensorFlow or PyTorch. If you’ve contributed to open-source ML projects or published research in scalable ML, include those details. Since this role involves both technical depth and collaboration, mention instances where you’ve worked cross-functionally or led initiatives. Lastly, align your education or industry experience with the preferred qualifications, such as advanced degrees or large-scale model training.
During the interview, expect deep technical discussions on machine learning infrastructure, scalability, and foundation models. Be prepared to walk through your experience with large-scale systems—interviewers will likely probe your problem-solving approach for handling massive datasets or optimizing model training. Practice explaining complex concepts clearly, such as how you’ve improved retrieval rates or reduced latency in past projects. Since teamwork is emphasized, be ready to discuss collaborative projects and how you’ve resolved conflicts or aligned stakeholders. Brush up on algorithms and data structures, as coding exercises may focus on efficiency. Lastly, research Apple’s recent work in AI to contextualize your answers—show enthusiasm for how your skills can contribute to their mission of integrating ML into products used by billions.