ML Engineer

We're seeking an experienced ML Engineer to develop and deploy intelligent systems for automated banner generation and adaptation. You'll build models that predict optimal element positioning when converting banners between different formats and dimensions, enabling scalable creative production.

Key Responsibilities

• Design and implement ML models for predicting banner element coordinates across different dimensions and formats

• Develop regression models to handle aspect ratio changes, scaling, and layout optimization

• Build data pipelines for training data generation from existing banner designs

• Create evaluation metrics to measure layout quality and design consistency

• Deploy models to production with low-latency inference requirements

• Collaborate with designers to understand layout principles and constraints

• Implement A/B testing frameworks to validate model performance

• Optimize models for different banner types (display ads, social media, web banners)

Required Qualifications

• 3+ years of experience in machine learning engineering

• Strong proficiency in Python and ML frameworks (TensorFlow, PyTorch, or scikit-learn)

• Experience with regression models and multi-task learning

• Understanding of computer vision and spatial reasoning problems

• Proficiency with gradient boosting methods (XGBoost, LightGBM, CatBoost)

• Experience deploying ML models to production environments

• Strong understanding of feature engineering and model evaluation

• Familiarity with MLOps practices and tools (MLflow, Kubeflow, etc.)

Preferred Qualifications

• Experience with design automation or creative generation systems

• Knowledge of UI/UX principles and layout design

• Experience with transformer architectures or attention mechanisms

• Background in computer graphics or computational design

• Experience with constraint optimization problems

• Familiarity with advertising technology or martech platforms

• Understanding of responsive design principles

Technical Skills

• ML/DL: PyTorch, TensorFlow, scikit-learn, XGBoost

• Languages: Python, SQL

• MLOps: Docker, Kubernetes, CI/CD pipelines

• Cloud: AWS/GCP/Azure ML services

• Data: Pandas, NumPy, data versioning (DVC)

• Monitoring: Model performance tracking, experiment tracking

What We Offer

• Competitive salary and equity

• Opportunity to work on cutting-edge ML applications in creative tech

• Collaborative team environment

• Professional development budget