Staff Applied Scientist

Adobe

Quick summary

Work type
On-site
Location
San Jose, CA
Salary
$216,400–$313,300 / yr
Posted
7 days ago

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $194k
This role $265k
$120k most similar roles pay here $334k

This role pays more than 92% of similar roles. Most pay $153,012–$234,837 — the shaded band above. At the midpoint, this role pays about $265k versus about $194k 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 303 open roles on FindRole.

Listed pay typically runs $183,300–$265,350 across 301 roles with salary data.

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At a glance

TL;DR · Staff Applied Scientist

As an Applied Scientist at Adobe Firefly’s ASML group, you will join a world-class team of researchers and engineers focused on advancing generative AI technology for image and video generation. Your role involves defining and driving the technical strategy for mid-training approaches to enhance editing capabilities across multimodal models, owning complex workstreams like image-to-image editing and cross-modal editing, and setting direction for large-scale captioning pipelines. You will also mentor junior engineers, influence leadership on investment priorities, and collaborate closely with research, data, evaluation, and infrastructure teams. Success in this role requires a Ph.D. in Computer Science or related field, deep expertise in generative architectures like diffusion models, extensive experience with PyTorch for distributed training, and strong software engineering skills in Python.

What you'll do

  • Define and drive technical strategy for mid-training approaches to enhance editing capabilities in multimodal generative models.
  • Own complex workstreams within the mid-training stack, making key architectural decisions for image-to-image editing and cross-modal tasks.
  • Set direction for large-scale captioning pipelines and lead VLM finetuning strategies to improve multimodal understanding across domains.
  • Drive end-to-end workflows for data curation, quality improvements, and distributed training, influencing infrastructure decisions.
  • Mentor junior engineers through design reviews and technical guidance, enhancing the team's overall capability.
  • Align research, data, evaluation, and pre/post-training teams on technical strategy and investment priorities.

What we're looking for

  • Ph.D. in Computer Science, Machine Learning, or related field with industry experience.
  • Deep expertise in generative architectures like diffusion models for image/video/audio.
  • Proven ability to architect and scale ML systems using PyTorch for distributed training.
  • Extensive VLM finetuning experience for multimodal understanding across visual/auditory domains.
  • Experience owning large-scale automated captioning pipelines for diverse datasets.
  • Strong software engineering skills in Python and production-quality system development.

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