Role Description & Responsibilities
Machine Learning
About Adobe
Adobe is a high-growth technology leader building scalable solutions for global users. Millions of companies and teams rely on their infrastructure and services to execute high-value workflows, analyze massive datasets, and scale modern operations.
CadetX Candidate Advantage: As an applicant for this Machine Learning role, you are evaluated on practical proficiency with real-world query execution, data pipelines, and clear stakeholder communication.
CadetX Role Brief & Company Specifications
Role Mission & Impact
As a Machine Learning Engineer Intern at Adobe, you will join an innovative team focused on building scalable, data-driven solutions. Develop, benchmark, and productionize machine learning and deep learning models to power intelligent features, recommendation systems, and automated decision engines.
This opening provides direct commercial exposure, structured technical mentorship, and the opportunity to contribute to production workflows across Adobe's core business domains.
Key Responsibilities & Day-to-Day Scope
- ✓ Prototype and evaluate modern ML and deep learning architectures for ranking, classification, or embedding generation using PyTorch or TensorFlow.
- ✓ Build scalable feature pipelines and data preprocessing workflows handling high-throughput realtime or batch streams.
- ✓ Conduct rigorous offline and online evaluations, ablation studies, and hyperparameter tuning to optimize model performance and latency.
- ✓ Collaborate with infrastructure engineers to deploy, containerize, and monitor models in production microservices.
- ✓ Stay current with contemporary machine learning literature and benchmark state-of-the-art architectures.
Official Qualifications & Technical Requirements
- • Enrolled in or completed an advanced degree (BS, MS, or PhD) in Computer Science, Machine Learning, Artificial Intelligence, Electrical Engineering, or related quantitative discipline.
- • High proficiency in Python and deep learning frameworks (PyTorch or TensorFlow).
- • Strong command of core data structures, algorithms, object-oriented design, and systems programming.
- • Solid theoretical understanding of loss functions, optimization (SGD, Adam), regularizations, and model evaluation metrics.
- • Experience training and validating models on real-world datasets.
Preferred Skills & Technology Stack
- + Familiarity with MLOps pipelines (Docker, Kubernetes, Kubeflow, Triton Inference Server).
- + Knowledge of distributed training techniques and GPU acceleration (CUDA).
- + Relevant coursework, research publication, or open-source contribution in ML/DL.
Tech Stack & Tools in this role
Expected technical competencies for Day 1 productivity:
★ Life & Culture at Adobe
Verified EmployerAdobe champions rigorous analytical discipline, continuous learning, and low-ego teamwork. Team members enjoy flexible hybrid allowances, learning stipends, and structured mentorship to fast-track career mobility.