Machine Learning Engineer 5

Adobe · Enterprise · Bangalore, India

Senior Machine Learning Engineer with expertise in generative modeling and computer vision to join Adobe's Applied AI team. The role involves architecting and shipping diffusion-based models, driving applied research into production, and mentoring engineers. Responsibilities include designing, training, and fine-tuning diffusion models for image, video, and multimodal generation, improving sampling efficiency, building production-grade pipelines for image/video understanding, developing and fine-tuning vision foundation models, integrating vision encoders with generative backbones, owning the full ML lifecycle, optimizing models for inference, designing scalable training infrastructure, and defining evaluation frameworks. The role also involves leadership, technical design reviews, and collaboration with product, research, and infrastructure teams.

What you'd actually do

  1. Design, train, and fine-tune large-scale diffusion models (DDPM, DDIM, LDM, DiT) for image, video, and multimodal generation tasks.
  2. Build production-grade pipelines for image/video understanding: segmentation, detection, depth estimation, optical flow, and 3D reconstruction.
  3. Own the full ML lifecycle: data curation, experiment tracking, model evaluation, optimization, deployment, and monitoring.
  4. Optimize models for inference: quantization (INT8/FP8), ONNX export, Flash Attention, and xFormers.
  5. Design scalable training infrastructure on distributed GPU clusters (DDP, FSDP, DeepSpeed) across thousands of GPU-hours.

Skills

Required

  • Python
  • PyTorch
  • score-based and diffusion models
  • computer vision fundamentals
  • CNNs
  • ViTs
  • feature pyramids
  • multi-scale processing
  • fine-tuning large vision and generative models
  • distributed training frameworks
  • DDP
  • FSDP
  • DeepSpeed
  • Megatron-LM
  • probabilistic ML
  • variational inference
  • information theory
  • MLOps tooling
  • Weights & Biases
  • MLflow
  • DVC

Nice to have

  • flow-based generative models
  • normalizing flows
  • CNFs
  • Rectified Flow
  • Flow Matching
  • video generation models
  • Sora-style architectures
  • CogVideo
  • AnimateDiff
  • SVD
  • 3D generative models
  • NeRF
  • 3D Gaussian Splatting
  • Zero-1-to-3
  • Point-E
  • multimodal systems
  • LLMs + vision
  • GPT-4V
  • LLaVA
  • InstructBLIP-style architectures
  • RLHF
  • DPO
  • open-source contributions
  • HuggingFace Diffusers
  • CompVis
  • timm

What the JD emphasized

  • architect and ship state-of-the-art diffusion-based models
  • drive applied research into production
  • translating the latest advances in generative AI into scalable, reliable systems
  • track record of shipping ML models to production at scale
  • mandatory proficiency in PyTorch
  • Deep theoretical and practical knowledge of score-based and diffusion models

Other signals

  • shipping state-of-the-art diffusion-based models
  • drive applied research into production
  • translating the latest advances in generative AI into scalable, reliable systems
  • own the full ML lifecycle
  • optimize models for inference
  • design scalable training infrastructure
  • define and instrument evaluation frameworks
  • track record of shipping ML models to production at scale