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, you'll 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

Bachelor's 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