This role pays more than
92%
of similar roles. Most pay
$202,612–$255,125
— the shaded band above.
At the midpoint, this role pays about
$320k
versus about
$229k
for comparable roles.
Based on 240 similar postings.
Employer
About Adobe
Adobe Inc. is a global software company known for creative and multimedia software products including Photoshop, Illustrator, Acrobat, and its cloud-based Creative Cloud and Document Cloud suites. Industry: Creative & Digital Experience Software
Adobe currently has
218 open roles
on FindRole.
Listed pay typically runs
$187,100–$270,950
across 216 roles with salary data.
The Principal Scientist, ML – Overall Architect joins the Applied Science & Machine Learning team as the primary technical authority bridging training and inference frameworks, model building, and data for next-generation video and image foundation models. This role involves owning end-to-end architecture to ensure coherence across large-scale distributed training systems, inference deployment, and data recipe design. The candidate will drive technical direction for PyTorch FSDP, tensor parallelism, pipeline parallelism, checkpointing, and fault tolerance while managing the co-design of model architectures with training infrastructure. Key responsibilities include making high-level decisions for GenRender6, Gen6.5, and GenEdit1 models to optimize for performance, scalability, and cost efficiency. The role requires expertise in generative AI, computer vision, and large-scale machine learning systems to resolve architectural tensions between modeling and data teams while ensuring seamless integration across the entire production pipeline.
Own the end-to-end technical architecture for training systems, model building, and data pipelines.
Provide technical direction for large-scale distributed training including FSDP, tensor parallelism, and fault tolerance.
Design and manage inference and serving infrastructure to ensure performance and cost-efficiency at scale.
Drive the co-design of model architectures, training recipes, and data designs to resolve technical tensions.
Serve as the primary authority to align cross-functional teams on interfaces and architectural requirements.
Make high-level architecture decisions for next-generation video and image foundation models.
Translate scaling and deployment requirements into concrete technical decisions to optimize model quality and cost.
What we're looking for
PhD in Computer Science, Electrical Engineering, AI/ML, or a related field, or equivalent depth through research or principal-level impact.
Demonstrated ability to provide technical direction across distributed training systems, inference infrastructure, model architecture, and data pipelines.
Hands-on expertise with large-scale distributed training including PyTorch FSDP, tensor parallelism, pipeline parallelism, checkpointing, and fault tolerance.
Proven track record of driving technical direction across multiple teams and resolving architectural conflicts in a complex organization.
Ability to make high-leverage architecture decisions for next-generation models while balancing performance, scalability, and cost efficiency.
Research contributions or publication record in generative AI, computer vision, or large-scale ML systems (preferred).
Experience owning end-to-end architecture for video, image, or multimodal foundation models from training through production deployment (preferred).
Track record of shipping large-scale generative models such as video generation, diffusion, or flow matching to production (preferred).
Remote (Sunnyvale, CA)+5176 days ago
$296,300–$453,200
Python
PyTorch
TensorFlow
C++
Distributed Training
DDP
FSDP
GPU Computing
AWS
GCP
Azure
Data Processing Pipelines
Model Optimization
Profiling
Debugging
Agentic AI
Machine Learning
Large Language Models
Reinforcement Learning
Amazon SageMaker
Amazon Bedrock
Kubernetes
Distributed Systems
Experimental Design