This role pays more than
78%
of similar roles. Most pay
$207,250–$260,450
— the shaded band above.
At the midpoint, this role pays about
$265k
versus about
$234k
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 Staff Applied Scientist joins the Adobe Firefly ASML group to advance generative AI technology for images and videos while ensuring high quality and control. This role sits at the intersection of research and engineering, where you will define technical strategies for multimodal data intelligence systems and architect distributed LLM/VLM inference platforms. You will build automated captioning, tagging, and metadata enrichment workflows while optimizing infrastructure for large-scale processing. Key responsibilities include managing petabyte-scale datasets, improving model efficiency, and developing scalable data generation pipelines. The role requires expertise in Python, PyTorch, and frameworks such as Ray, Spark, Dask, or Kubernetes. You will also utilize tools like vLLM, TensorRT-LLM, SGLang, and Triton Inference Server to solve complex problems involving multimodal foundation models, vector search infrastructure, and high-throughput data processing for large-scale media assets.
What does a Applied Scientist earn?
Median $208800 from 47 postings across 10 companies.
Architect and optimize distributed multimodal inference pipelines for large-scale image, video, and audio captioning and metadata generation.
Drive LLM/VLM inference optimization including batching, scheduling, quantization, and GPU utilization to maximize throughput and cost efficiency.
Build scalable data generation workflows using vision-language and multimodal foundation models to improve training data quality.
Lead technical strategy for automated dataset annotation, filtering, quality scoring, deduplication, and metadata enrichment.
Design distributed processing systems capable of handling billions of media assets across heterogeneous compute environments.
Productionize new LLM/VLM capabilities while ensuring scalability, reliability, and operational efficiency in collaboration with research teams.
Partner with infrastructure teams to improve distributed execution frameworks, storage systems, and inference services.
Mentor engineers in distributed systems, scalable ML infrastructure, and multimodal AI engineering best practices.
What we're looking for
Ph.D. or M.S. in Computer Science, Machine Learning, or a related technical field.
Significant industry experience designing and deploying large-scale distributed ML systems.
Deep expertise in LLMs, VLMs, or multimodal foundation models with experience serving inference workloads at scale.
Strong background in distributed systems, large-scale data processing, and cloud-native ML infrastructure using frameworks like Ray, Spark, Dask, or Kubernetes.
Proven experience optimizing LLM/VLM inference through batching, quantization, model serving, and GPU utilization.
Experience building high-throughput multimodal data pipelines for tasks such as captioning, tagging, and metadata extraction.
Hands-on experience with modern ML inference frameworks like vLLM, TensorRT-LLM, SGLang, Triton, or TGI.
Proficiency in Python and PyTorch to develop production-grade distributed ML systems.
Python
C++
Unreal Engine
Unity
Blender
CARLA
Isaac Sim
Omniverse
LLM
Diffusion Models
Multimodal Learning
Computer Graphics
Procedural Generation
Ray Tracing
Shader Development
Data Pipelines
Simulation
Agentic Systems