Senior Applied Machine Learning Engineer, Typographic Intelligence

Adobe Adobe · Enterprise · San Jose, CA

Senior Applied Machine Learning Engineer to design and deliver intelligent typographic features across Adobe's creative products, simplifying decisions and workflows for millions of users. This role involves applying advanced modeling to design, train, debug, and operationalize ML systems for layout, generative styling, and design intelligence, owning the modeling lifecycle end-to-end and integrating models into hybrid product architectures.

What you'd actually do

  1. Model typographic layout, styling, and generative workflows; convert research ideas into reproducible implementations and product features.
  2. Build and maintain end-to-end training pipelines including data ingestion, feature engineering, training, validation, and artifact management.
  3. Perform model debugging and error analysis (ablation studies, failure analysis, metric design) and tune hyperparameters for performance and robustness.
  4. Optimize training and inference (mixed precision, quantization, distillation, pruning) with attention to latency, cost, and scalability.
  5. Productionize models and inference services; implement monitoring for model drift and data quality.

Skills

Required

  • Strong Python skills
  • hands-on experience with PyTorch, TensorFlow, or similar frameworks
  • Experience debugging models, tuning hyperparameters, and improving model performance in production environments
  • Experience building data pipelines and model serving infrastructure
  • Strong software engineering fundamentals (testing, version control, code reviews, CI/CD)
  • Experience working with engineering teams to integrate modeling solutions into large-scale product systems
  • Comfortable in an R&D and iteration-heavy environment and able to move prototypes toward reliable production systems

Nice to have

  • Experience with diffusion models, GANs, or reinforcement learning
  • Computer vision experience (segmentation, masking, OpenCV, Detectron2, SAM, YOLO)
  • Experience optimizing models for production (GPU acceleration, quantization, distillation)
  • Background in recommendation systems, ranking, or NLP
  • MS or PhD in Computer Science, ML, or related field

What the JD emphasized

  • 5-8 years building and operating production ML systems
  • Demonstrated ownership across the modeling lifecycle: data -> modeling -> training -> deployment -> monitoring
  • Strong software engineering fundamentals (testing, version control, code reviews, CI/CD)

Other signals

  • design and deliver intelligent typographic features
  • apply advanced modeling to design, train, debug, and operationalize ML systems
  • own the modeling lifecycle end-to-end
  • move ML systems from prototype to reliable product capabilities