Machine Learning Engineer Intern (Summer 2026)

Cognitiv•Bellevue, WA

1d•$30 - $32•Hybrid

About The Position

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! Hybrid: Bellevue, WA (MTW in-office) Internship Duration: June 1 - August 14, 2026 The Role We are bringing on an MLE intern this summer to achieve the following: Fresh Perspective on SOTA Research: Bring in new academic insights, especially in areas like Transformer-based feature generation or efficient DL training, to challenge our current paradigms. Talent Pipeline: As our system scales to tens of billions of data points, we are looking for high-potential talent to tackle future architectural challenges. Accelerated Innovation: Utilize the summer window to explore "high-reward" exploratory projects that are crucial for our long-term roadmap and require dedicated, deep-dive research.

Requirements

Currently pursuing a BS, MS, or PhD in Computer Science or a related technical field.
Strong programming skills in Python.
Experience with machine learning frameworks such as PyTorch or TensorFlow.
Familiarity with data processing and building ML workflows or pipelines.
Strong understanding of algorithms, data structures, and software engineering fundamentals.

Nice To Haves

Experience with large-scale data systems or distributed processing (e.g., Spark, Flink, Ray).
Exposure to ML systems or infrastructure (feature pipelines, training pipelines, model evaluation).
Interest or experience in modern ML approaches (e.g., Transformers, embeddings, LLM-related workflows).
Familiarity with cloud environments or production systems (e.g., AWS, Kubernetes, CI/CD).

Responsibilities

Work with a mentor on project planning and execution, including regular check-ins and weekly 1:1s.
Write code to build and improve ML infrastructure and feature pipelines.
Perform data exploration, feature engineering, and monitoring features for data quality.
Collaborate with ML scientists and engineers in daily standups and weekly meetings.
Present progress and final project results