Applied Scientist 5.5

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.

Requirements

• 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
• About Adobe
• Let's Adobe together
• At Adobe, we believe in creating a company culture where all employees

Benefits

Additional Information

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.

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