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$202,500–$255,000.
Based on 240 similar postings.
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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
282 open roles
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Listed pay typically runs
$148,100–$282,100
across 76 roles with salary data.
The Staff Applied Scientist - VLLM Inference joins the Adobe Firefly ASML group to advance generative AI technology for images and videos. In this role, you will define technical strategy for multimodal data intelligence systems and architect distributed LLM/VLM inference platforms. You will build scalable data generation workflows, optimize batching, quantization, and GPU utilization, and develop solutions for automated captioning and metadata enrichment. Key responsibilities include productionizing new capabilities and managing petabyte-scale datasets across heterogeneous compute environments. You must possess deep expertise in vision-language models, distributed systems, and cloud-native ML infrastructure. Required technologies include Python, PyTorch, Ray, Spark, Dask, Kubernetes, vLLM, TensorRT-LLM, SGLang, and Triton Inference Server. This position solves complex problems regarding model efficiency, data quality, and scalable AI infrastructure for multimodal content.
What does a Applied Scientist earn?
Median $208800 from 61 postings across 10 companies.
Architect and optimize distributed multimodal inference pipelines for large-scale image, video, and audio captioning and tagging.
Drive LLM/VLM inference optimization including batching, 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, and metadata enrichment across multimodal datasets.
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.
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 building and 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 including batching, parallelism, quantization, and GPU utilization.
Experience building high-throughput multimodal data pipelines for automated image, video, and audio understanding tasks.
Hands-on experience with ML inference and serving frameworks such as vLLM, TensorRT-LLM, SGLang, Triton Inference Server, or TGI.
Strong software engineering skills in Python and PyTorch to develop production-grade distributed ML systems.