Machine Learning Engineer

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