Machine Learning Research Scientist Intern (Fall 2025)

Full-time

Job Description

Are you ready to revolutionize the advertising industry?

At Cognitiv, we are not just another AdTech company—we are industry trailblazers redefining media buying with our Deep Learning Advertising Platform. Since 2015, we have harnessed the power of cutting-edge deep learning technology and data science to transform how brands connect with their customers. Our mission? To bring intelligence to advertising and deliver unparalleled precision, relevance, and impact at scale.

With our innovative platform, advertisers enjoy unprecedented flexibility—whether it is activating Dynamic Deals through their preferred DSP, leveraging our managed service DSP, or utilizing our industry-first ContextGPT product. As a part of Cognitiv, you will be at the forefront of AI-driven advertising solutions, driving change and achieving remarkable growth in a rapidly evolving industry.

Now, we’re growing—and looking for passionate team members to help shape the future.

The Role

We are looking for a Machine Learning Research Intern to join our AdTech research team for 2025 Fall. This internship is ideal for students or recent graduates passionate about deep learning, model optimization, and performance tuning in real-world applications. You will work closely with our ML Research Scientists to enhance Deep Neural Network (DNN) models, focusing on improving key metrics in our real-time bidding (RTB) algorithms and Large Language Model (LLM) integrations.

This role provides an opportunity to gain hands-on experience in optimizing deep learning architectures while contributing to cutting-edge ML solutions in AdTech. You will participate in research discussions, implement and evaluate model improvements, and collaborate with ML engineers to refine training pipelines.

This position will be located in San Mateo, CA with a hybrid work schedule of 3 days in office (Mon/Tue/Wed) and 2 days remote (Thursday/Friday). We are also open to remote applicants.

What You Will Be Doing

Optimize ML models – Improve predictive accuracy, inference speed, and efficiency in AdTech applications.

Experiment & tune – Run hyperparameter tuning, explore new architectures, and fine-tune models to move the needle on key business metrics.

Build strong datasets – Help construct and preprocess high-quality training data for ML pipelines.

Run experiments – Conduct PyTorch experiments and evaluate results using clear, measurable metrics.

Deploy at scale – Collaborate with researchers and engineers to build scalable ML pipelines for smooth deployment and iteration.

Stay ahead of the curve – Keep up with deep learning research and propose novel approaches for model improvement.

Communicate findings – Present research and experimental outcomes in clear reports that influence decision-making.

Who You Are

Educational foundation – Pursuing or recently completed a Master’s or Ph.D. in CS, Stats, EE, or a related field.

Deep learning fluent – Strong grasp of architectures like Transformers and hands-on with PyTorch.

Experimentation-driven – Skilled in hyperparameter tuning, loss function design, and optimizing training pipelines.Python-first – Proficient in Python for data manipulation and ML experimentation.

Broad ML knowledge – Familiar with techniques like gradient boosting (XGBoost), PCA, and distributed training.

Cloud & data aware – Comfortable with AWS/GCP and have some exposure to Spark, Hadoop, or SQL.

Strong communicator – Can explain technical results clearly and thrive in a collaborative, fast-paced environment.