About the role
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
