Responsibilities

• Optimize deep learning models to improve predictive accuracy, inference speed, and efficiency in AdTech applications.
• Work on hyperparameter tuning, model architecture exploration, and fine-tuning techniques to achieve better performance on key business metrics.
• Assist in constructing and preprocessing datasets for model training, ensuring high-quality inputs for ML pipelines.
• Conduct experiments using PyTorch and evaluate performance through well-defined metrics.
• Collaborate with researchers and engineers to develop scalable ML pipelines, ensuring smooth model deployment and updates.
• Stay updated with the latest deep learning research and propose innovative techniques for model improvements.
• Present research findings and experimental results in clear, concise reports to drive informed decision-making.

Requirements

• Pursuing or recently completed a Master’s or Ph.D. in Computer Science, Statistics, Electrical Engineering, or a related field.
• Solid foundation in deep learning architectures (e.g., Transformers) and familiarity with frameworks like PyTorch
• Hands-on experience in hyperparameter tuning, loss function design, and training pipeline optimization.
• Strong proficiency in Python for data manipulation and ML experimentation.
• Familiarity with common ML techniques such as gradient boosting (e.g., XGBoost), PCA, and distributed training.
• Basic understanding of cloud platforms (AWS, GCP) and big data processing (Spark, Hadoop, SQL) is a plus.
• Good communication skills and willingness to work in a fast-paced, collaborative research environment.

Preferred Qualifications

• Experience with LLMs and fine-tuning transformer-based models is a plus.
• Familiarity with AdTech, real-time bidding (RTB), or online advertising models is a bonus.
• Exposure to C++ for ML performance optimization is a significant advantage.

Salary

• $30-$32/HR