Applied Scientist 5.5

We are looking for an Applied Scientist with deep expertise in generative modeling and computer vision to join Adobe's Applied AI team. In this role, you will architect and ship state-of-the-art diffusion-based models, drive applied research into production, and mentor a team of talented engineers. You will work at the intersection of cutting-edge research and real-world impact — translating the latest advances in generative AI into scalable, reliable systems.
Key Responsibilities
Generative Modeling
Design, train, and fine-tune large-scale
diffusion models
(DDPM, DDIM, LDM, DiT) for image, video, and multimodal generation tasks.
Drive improvements in sampling efficiency — distillation, consistency models, progressive training, and guided generation techniques.
Stay current with and rapidly prototype ideas emerging from the broader AI community.
Computer Vision & Perception
Build production-grade pipelines for image/video understanding: segmentation, detection, depth estimation, optical flow, and 3D reconstruction.
Develop and fine-tune vision foundation models (ViT, CLIP, DINOv2, SAM) for downstream tasks using parameter-efficient methods (LoRA, adapters).
Integrate vision encoders with generative backbones for controllable generation (ControlNet, IP-Adapter, inpainting, editing).
Applied ML & Systems
Own the full ML lifecycle: data curation, experiment tracking, model evaluation, optimization, deployment, and monitoring.
Optimize models for inference: quantization (INT8/FP8), ONNX export, Flash Attention, and xFormers.
Design scalable training infrastructure on distributed GPU clusters (DDP, FSDP, DeepSpeed) across thousands of GPU-hours.
Define and instrument evaluation frameworks, benchmarks, and human preference studies (RLHF / DPO) to measure generative quality.
Leadership & Collaboration
Lead technical design reviews, write engineering RFCs, and set quality standards for the team.
Mentor junior and mid-level ML engineers through code reviews, 1:1s, and pair-programming sessions.
Collaborate with product, research, and infrastructure teams to translate research ideas into shipped features.
Required Qualifications
15+ years of hands-on ML engineering experience in industry or research.
MS or PhD in Computer Science, Machine Learning, Statistics, or equivalent practical experience.
Expert-level Python; strong, mandatory proficiency in
PyTorch
.
Deep theoretical and practical knowledge of
score-based and diffusion models
.
Strong background in computer vision fundamentals: CNNs, ViTs, feature pyramids, multi-scale processing.
Experience fine-tuning large vision and generative models at scale.
Proficiency with distributed training frameworks (DDP, FSDP, DeepSpeed, Megatron-LM).
Solid grasp of probabilistic ML, variational inference, and information theory.
Experience with MLOps tooling (Weights & Biases, MLflow, DVC, or equivalent).
Track record of shipping ML models to production at scale.
Excellent written and verbal communication skills with cross-functional stakeholders.
Preferred Qualifications
Experience with
flow-based generative models
(normalizing flows, CNFs, Rectified Flow, Flow Matching).
Experience with
video generation models
(Sora-style architectures, CogVideo, AnimateDiff, SVD).
Familiarity with
3D generative models
(NeRF, 3D Gaussian Splatting, Zero-1-to-3, Point-E).
Background in
multimodal systems
(LLMs + vision: GPT-4V, LLaVA, InstructBLIP-style architectures).
Experience with RLHF / DPO for generative model alignment and preference optimization.
Active open-source contributions — maintained repos, significant PRs to projects like HuggingFace Diffusers, CompVis, timm, or similar.
Active GitHub presence demonstrating ongoing engagement with the ML community.
Equal Opportunity Statement
Adobe is an equal opportunity employer. We celebrate diversity and are committed to building an inclusive environment for all employees. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
About Adobe
Adobe empowers everyone to create through innovative platforms and tools that unleash creativity, productivity and personalized customer experiences. Adobe’s industry-leading offerings including Adobe Acrobat Studio, Adobe Express, Adobe Firefly, Creative Cloud, Adobe Experience Platform, Adobe Experience Manager, and GenStudio enable people and businesses to turn ideas into impact, powered by AI and driven by human ingenuity.
Our 30,000+ employees worldwide are creating the future and raising the bar as we drive the next decade of growth. We’re on a mission to hire the very best and believe in creating a company culture where all employees are empowered to make an impact. At Adobe, we believe that great ideas can come from anywhere in the organization. The next big idea could be yours.
Let’s Adobe together
At Adobe, we believe in creating a company culture where all employees are empowered to make an impact. Learn more about Adobe life, including our
values and culture
,
focus on people, purpose and community
,
Adobe for All
, comprehensive
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, the
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, the
customers
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Equal Employment Opportunity
employer. We do not discriminate based on gender, race or color, ethnicity or national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status, or any other protected characteristic.
Learn more.
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or call +1 408-536-3015.
AI Use Guidelines for Interviews:
Our interviews are designed to reflect your own skills and thinking. The use of AI or recording tools during live interviews is not permitted unless explicitly invited by the interviewer or approved in advance as part of a reasonable accommodation. If these tools are used inappropriately or in a way that misrepresents your work, your application may not move forward in the process.
At Adobe, we empower employees to innovate with AI — and we look for candidates eager to do the same. As part of the hiring experience, we provide clear guidance on where AI is encouraged during the process and where it’s restricted during live interviews. See how we think about
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