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.
Description
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.
Minimum Qualifications
Bachelors 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 JAX
Preferred Qualifications
MS 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