Machine Learning Engineer - Video Generation Models

We are hiring a machine learning engineer with deep, hands-on experience training large generative models to help build our video generation models. You will work across pre-training, fine-tuning, and inference optimization, from designing the training recipe and running large distributed training jobs through making the resulting models efficient to run. As a member of the team, you will develop fundamental model capabilities and collaborate with engineers and researchers across Apple to advance our products. As a member of our fast-paced group, youll have the unique and rewarding opportunity to shape upcoming products from Apple. We are looking for someone who has taken large generative models through the full lifecycle, from pre-training through fine-tuning and efficient inference, and can bring that depth to video, with the engineering skills to make that work reproducible and production-ready.Pre-training video generation models, including architecture selection, training recipe design, hyperparameter and scaling decisions, and evaluation of model quality

Running, monitoring, and debugging large-scale distributed training jobs, and diagnosing loss instabilities, divergence, and throughput regressions

Improving training efficiency and cost, including parallelism strategy, mixed-precision training, checkpointing, and hardware utilization

Improving inference efficiency through step distillation, few-step sampling, and quantization, and characterizing the resulting quality and latency trade-offs

Adapting pre-trained models through fine-tuning, preference optimization, and knowledge distillation

Building and maintaining the training and evaluation code the team depends on, with an emphasis on reproducibility and reliability

Machine Learning, Engineering, Recipe Development, Machine Learning, Apple, Debugging Skills, Identify Issues, Electrical Engineering, Computer Engineering, Requirements Management, Partnering with product and research stakeholders to translate requirements into modeling and engineering tasksBachelors degree in Electrical Engineering, Computer Science, Computer Engineering, or relevant degree, and a minimum of 3 years relevant industry experience

Experience with large-scale generative model training for video generation

Experience running distributed training across multi-node GPU clusters

Strong software engineering skills in Python, with proficiency in a modern deep learning framework such as PyTorch or JAXMS or PhD in Electrical Engineering, Computer Science, or Computer Engineering

Experience with video generation architectures, including diffusion or autoregressive models, temporal consistency, and long-horizon generation

Experience contributing to major foundation or base model pre-training efforts, including scaling laws and transferring training recipes across model and training scales

Experience with large-scale training operations, including parallelism strategies and diagnosing loss instability, divergence, or throughput regressions

Experience improving and adapting trained models, such as step distillation, few-step sampling, or quantization for inference efficiency, and supervised fine-tuning, preference optimization, or knowledge distillation for quality

Ability to work through ambiguity, collaborate across teams and disciplines, and communicate complex technical results clearly