Machine Learning Engineer 4

Adobe Adobe · Enterprise · Noida, India

Machine Learning Engineer to develop and optimize ML models for search, recommendation, and content understanding, build and deploy scalable generative AI solutions, and integrate ML models into production systems. The role involves extensive data handling, indexing billions of assets, applying ML to improve ranking and recommendations, researching and implementing techniques in language comprehension, computer vision, and multimodal learning, and contributing to the end-to-end ML pipeline. Focus on real-time, large-scale applications and translating customer needs into solutions.

What you'd actually do

  1. develop and optimize machine learning models and algorithms for search, recommendation, and content understanding across diverse content types.
  2. build and deploy scalable generative AI solutions.
  3. integrate ML models into production systems, ensuring high performance, reliability, and user impact.
  4. researching, crafting, and implementing groundbreaking techniques in language comprehension, computer vision, and multimodal learning for content and asset discovery.
  5. contribute to the end-to-end ML pipeline — including data preprocessing, model training, evaluation, deployment, and monitoring — while pushing the boundaries of computational efficiency to meet the needs of real-time, large-scale applications.

Skills

Required

  • Python (for ML/AI)
  • machine learning frameworks and tools (e.g., TensorFlow, PyTorch)
  • deep learning techniques for computer vision (e.g., CNNs, transformers)
  • natural language understanding (e.g., BERT, GPT)
  • multimodal AI
  • processing data at a large scale
  • AWS resources
  • search engine technology
  • large-scale distributed systems and frameworks (e.g., Kubernetes, Spark, Hadoop)

Nice to have

  • generative AI (e.g., Stable Diffusion, DALL·E, Midjourney)
  • Elastic Search/Solr
  • computational geometry, 3D modeling, or animation pipelines
  • real-time recommendation systems
  • search indexing
  • Web services and REST
  • RDBMS and NoSQL databases
  • Java
  • Publications in peer-reviewed journals or conferences

What the JD emphasized

  • building and deploying machine learning systems at scale
  • engineering SaaS-based software
  • delivering ML solutions to production environments
  • processing data at a large scale
  • large-scale distributed systems and frameworks

Other signals

  • Generative AI powerhouse
  • unified Agentic platform
  • Intelligent Content Processing
  • real-time systems
  • generative AI solutions
  • content discovery
  • contextual recommendations
  • multimodal learning
  • end-to-end ML pipeline
  • large-scale applications
  • customer needs
  • ML models into production systems
  • search, recommendation, and content understanding