Machine Learning Engineer 5

About the Role We are looking for a Senior Machine Learning Engineer 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 8–10 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 discrimi ... (truncated, view full listing at source)