AIML - Software Engineer
Job Description
Summary
Apple Intelligence is pushing the boundary of Machine Learning by bringing Generative AI to consumers in a way that enhances people\'s lives while guaranteeing their privacy and safety. To deliver this technology at the high standards that Apple is known for, we are investing in the foundational ML infrastructure for training and evaluating models in Apple Intelligence. As part of this effort, the Systems team in Siri focuses on improving efficiency of end-to-end ML pipelines in order to both optimize cloud compute resource utilization and maximize engineering productivity. The Systems team combines a wide variety of skills ranging from low-level optimization of CUDA kernels over improving model implementation for better training efficiency to architecting datagen-training-evaluation pipelines.
Description
The Systems team is looking for a skilled and highly motivated ML Software Engineer to support our efforts on training efficiency. In this role, you will be responsible for evaluating and enabling hardware accelerators to optimize the training and inference of deep learning models at Apple. This is a highly cross-functional role that requires close collaboration and coordination with the ML modeling team, compute platform team, and external partners. If you are excited about Machine Learning efficiency and want to have high impact at the core of Apple Intelligence, we want to hear from you!
Key Responsibilities
- Evaluate and integrate hardware accelerators for the training and inference of deep learning models.
- Collaborate with cross-functional teams to prototype and develop solutions that enhance the performance of our ML stack.
- Integrate new technologies (eg. torch.compile, XLA, Model Parallelism, Tensor Parallelism, FP8) into the existing codebase to enable large-scale deployment of ML models, significantly reducing engineering iteration time.
- Profile system performance, identify bottlenecks, and optimize runtime performance.
- Work closely with external partners to ensure smooth integration of hardware accelerators with our existing infrastructure.
- Stay up-to-date with the latest advancements in hardware accelerators and deep learning technologies.
Key Responsibilities
- Evaluate and integrate hardware accelerators for the training and inference of deep learning models.
- Collaborate with cross-functional teams to prototype and develop solutions that enhance the performance of our ML stack.
- Integrate new technologies (eg. torch.compile, XLA, Model Parallelism, Tensor Parallelism, FP8) into the existing codebase to enable large-scale deployment of ML models, significantly reducing engineering iteration time.
- Profile system performance, identify bottlenecks, and optimize runtime performance.
- Work closely with external partners to ensure smooth integration of hardware accelerators with our existing infrastructure.
- Stay up-to-date with the latest advancements in hardware accelerators and deep learning technologies.
Minimum Qualifications
- Excellent Python coding skills
- Experience with GPUs, TPUs, AWS Trainium
- Familiarity with PyTorch
- Excellent interpersonal skills
Preferred Qualifications
- ML System experience - Large Scale distributed Training and Inference.
- Familiarity with JAX, XLA, HLO, Torch Compile
- Experience implementing/optimizing CUDA kernels
