Senior Applied Scientist

Adobe

Quick summary

Work type: On-site
Location: Seattle, WA
Salary: $168,600–$244,200 / yr
Posted: 4 days ago
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $188k

This role $206k

$131k most similar roles pay here $256k

This role pays more than 70% of similar roles. Most pay $162,000–$213,931 — the shaded band above. At the midpoint, this role pays about $206k versus about $188k 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 301 open roles on FindRole.

Listed pay typically runs $183,300–$265,350 across 299 roles with salary data.

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At a glance

TL;DR · Senior Applied Scientist

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Adobe Firefly’s Applied Science & Machine Learning (ASML) group seeks a research scientist or engineer to focus on post-training, alignment, and distillation of large-scale generative AI models. This role involves conducting innovative R&D in techniques like RLHF, DPO/GRPO, SFT, and model efficiency methods to enhance the quality, safety, and scalability of Firefly’s image and video generation capabilities. Day-to-day responsibilities include designing evaluation pipelines, collaborating with cross-functional teams, and implementing research ideas into production using Python and modern ML toolkits. Ideal candidates hold a Master’s or Ph.D. in Computer Science or related fields, with expertise in large-scale generative AI training and post-training methods, proficiency in Python, PyTorch, and ML infrastructure tools, and a strong publication record.

Skills

Python PyTorch RLHF DPO/GRPO SFT diffusion models transformers CI/CD ML infrastructure tools TensorFlow Kubernetes AWS PostgreSQL

What you'll do

Conduct research and development in post-training alignment for large-scale generative AI models.
Design and evaluate techniques like RLHF, DPO/GRPO, SFT to enhance model controllability and safety.
Develop methods for efficient distillation and inference acceleration of frontier models at scale.
Build evaluation pipelines to assess generative models across quality, efficiency, and safety metrics.
Convert research ideas into production-ready implementations using Python and modern ML toolkits.

What we're looking for

Master’s or Ph.D. in Computer Science, Machine Learning, or related field.
Expertise in large-scale generative AI post-training techniques (SFT, RLHF, DPO/GRPO).
Experience with diffusion models, transformers, and other advanced generative architectures.
Strong coding skills in Python, PyTorch, and ML infrastructure tools.
Ability to collaborate effectively across cross-functional teams.
Excellent communication skills for technical mentorship and guidance.

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