About the job
Exciting opportunity for a Senior Applied Machine Learning Engineer to design and deploy intelligent typographic features at scale. Join our team to drive innovation in generative AI, production ML, and product integration for millions of users. Collaborate with top engineers and shape the future of creative technology at Adobe.
Responsibilities
Model typographic layout, styling, and generative workflows; convert research ideas into reproducible implementations and product features.
Build and maintain end-to-end training pipelines including data ingestion, feature engineering, training, validation, and artifact management.
Perform model debugging and error analysis (ablation studies, failure analysis, metric design) and tune hyperparameters for performance and robustness.
Optimize training and inference (mixed precision, quantization, distillation, pruning) with attention to latency, cost, and scalability.
Productionize models and inference services; implement monitoring for model drift and data quality.
Collaborate with computer science and product engineers to integrate modeling solutions into client and server architectures (APIs, SDKs, services).
Write production-quality code, tests, and automation for labeling and evaluation; follow CI/CD best practices.
Prototype generative approaches (diffusion, GANs, multimodal transformers) and apply reinforcement learning or bandit methods where appropriate for interactive optimization.
Qualifications
Minimum
5-8 years building and operating production ML systems.
Strong Python skills and hands-on experience with PyTorch, TensorFlow, or similar frameworks.
Demonstrated ownership across the modeling lifecycle: data -> modeling -> training -> deployment -> monitoring.
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.
Preferred
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.