This role pays less than
64%
of similar roles. Most pay
$195,150–$254,750
— the shaded band above.
At the midpoint, this role pays about
$203k
versus about
$225k
for comparable roles.
Based on 240 similar postings.
Employer
About Apple Inc
Apple Inc. is a multinational technology company known for designing and manufacturing consumer electronics, software, and online services, including the iPhone, Mac, iPad, and App Store. Industry: Consumer Electronics & Software
Apple Inc currently has
2000 open roles
on FindRole.
Listed pay typically runs
$175,000–$277,600
across 1613 roles with salary data.
Machine Learning Engineer - Video Generation Models will join a fast-paced team to develop and optimize video generation models through the entire lifecycle, including pre-training, fine-tuning, and inference optimization. The role involves designing training recipes, managing large distributed training jobs across multi-node GPU clusters, and improving efficiency through techniques like mixed-precision training, quantization, and step distillation. You will build and maintain reliable evaluation code while collaborating with researchers to translate requirements into technical tasks. Key responsibilities include addressing loss instabilities, managing scaling decisions, and ensuring temporal consistency in video generation. The ideal candidate possesses strong software engineering skills in Python and proficiency in PyTorch or JAX. Experience with diffusion or autoregressive models is highly valued as you work to make large-scale generative models reproducible and production-ready for upcoming products.
What does a Machine Learning Engineer earn in California?
Median $246150 from 178 postings across 30 companies.
Design training recipes, architecture selections, and hyperparameter scaling for video generation models.
Run, monitor, and debug large-scale distributed training jobs across multi-node GPU clusters.
Optimize training efficiency through parallelism strategies, mixed-precision training, and improved hardware utilization.
Improve inference performance using techniques like step distillation, few-step sampling, and quantization.
Adapt pre-trained models through fine-tuning, preference optimization, and knowledge distillation.
Build and maintain reliable, reproducible training and evaluation codebases for the team.
Translate product and research requirements into specific modeling and engineering tasks.
What we're looking for
Bachelor's degree in Electrical Engineering, Computer Science, Computer Engineering, or a relevant field.
Minimum of 3 years of 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 and proficiency in PyTorch or JAX.
MS or PhD in Electrical Engineering, Computer Science, or Computer Engineering (preferred).
Experience with video generation architectures, including diffusion or autoregressive models, temporal consistency, and long-horizon generation (preferred).
Experience with large-scale training operations, inference optimization techniques, and model adaptation methods like fine-tuning or distillation (preferred).
Machine Learning
Computer Vision
Generative AI
Python
PyTorch
C++
Diffusion Models
3D Reconstruction
Neural Radiance Fields
3D Gaussian Splatting
Differentiable Rendering
Distributed Training
On-device ML