Job Overview:
The Sr Engineering Program Manager for ML Compute Infrastructure at Apple’s AIML team will drive the development of scalable and efficient compute solutions for machine learning workflows, overseeing capacity expansions, accelerator hardware strategy, and cross-functional partnerships. This role requires translating ML platform requirements into infrastructure programs, evaluating emerging technologies like GPUs and TPUs, and ensuring seamless integration of hardware and software at scale. The ideal candidate will have a strong background in large-scale compute architecture, vendor management, and AI/ML frameworks, with a focus on optimizing price/performance for Apple’s AI initiatives. Collaboration with internal teams and external cloud providers like AWS and GCP is key, along with a deep understanding of hybrid or on-prem ML infrastructure scaling.
>> View full job details on Apple’s official website.
Resume and Interview Tips:
To tailor your resume for the Sr Engineering Program Manager role at Apple’s AIML team, emphasize your experience in managing large-scale ML compute infrastructure and cross-functional projects. Highlight specific instances where you led capacity expansions, evaluated accelerator technologies, or optimized price/performance for ML workloads. Quantify your achievements, such as cost savings or efficiency improvements, to demonstrate impact. Showcase your familiarity with AI/ML frameworks like TensorFlow and PyTorch, as well as your ability to collaborate with cloud providers and hardware vendors. If you have experience with data center expansions or high-density compute deployments, make sure to include those details. Your resume should reflect both your technical expertise and your leadership in driving infrastructure programs from vision to execution.
During the interview, expect questions about your experience with ML compute infrastructure, including how you’ve evaluated and integrated accelerator technologies like GPUs and TPUs. Be prepared to discuss specific projects where you balanced power, performance, and cost for large-scale ML workloads. The interviewer will likely probe your ability to manage cross-functional teams and vendor partnerships, so have examples ready that demonstrate your leadership in these areas. Technical questions may cover your understanding of AI/ML frameworks, high-performance computing environments, and infrastructure orchestration. Practice articulating your vision for scaling ML infrastructure and how you mitigate technical risks early in projects. Dress professionally and be ready to discuss your approach to aligning hardware procurement, rack deployment, and software readiness for seamless delivery at scale.