Applied Scientist 5.5

Adobe Adobe · Enterprise · Bangalore, India +1

This role focuses on designing, training, and fine-tuning large-scale diffusion models for image, video, and multimodal generation tasks, and building production-grade pipelines for computer vision tasks. It involves optimizing models for inference, defining evaluation frameworks, and shipping ML models to production.

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. Optimize models for inference: quantization (INT8/FP8), ONNX export, Flash Attention, and xFormers.
  4. Define and instrument evaluation frameworks, benchmarks, and human preference studies (RLHF / DPO) to measure generative quality.
  5. Lead technical design reviews, write engineering RFCs, and set quality standards for the team.

Skills

Required

  • Python
  • PyTorch
  • score-based and diffusion models
  • computer vision fundamentals
  • fine-tuning large vision and generative models
  • distributed training frameworks
  • probabilistic ML
  • variational inference
  • information theory
  • MLOps tooling

Nice to have

  • flow-based generative models
  • video generation models
  • 3D generative models
  • multimodal systems
  • RLHF / DPO
  • Active open-source contributions
  • Active GitHub presence

What the JD emphasized

  • shipping state-of-the-art diffusion-based models
  • drive applied research into production
  • translating the latest advances in generative AI into scalable, reliable systems
  • Build production-grade pipelines for image/video understanding
  • Optimize models for inference
  • Define and instrument evaluation frameworks
  • Track record of shipping ML models to production at scale

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
  • Build production-grade pipelines for image/video understanding
  • Optimize models for inference
  • Define and instrument evaluation frameworks
  • Track record of shipping ML models to production at scale