Machine Learning Engineer Intern (Summer 2026) at Cognitiv · Bellevue

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.

What You’ll Do

• 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

Tech Stack

• Languages: Python
• ML Frameworks: PyTorch
• Data Processing: Spark, Ray, or similar distributed frameworks.
• Streaming: Apache Flink, Kafka, or similar systems.
• Infrastructure: Kubernetes, cloud platforms, distributed storage.
• DevOps & Tooling: Git, CI/CD pipelines, GitHub Actions.

Who You Are

• 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.

Bonus Points:

• 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).

Why Join Us?

This internship offers a rare combination of cutting-edge ML research and real-world production systems. Interns will work with large-scale data, modern ML techniques like embeddings and Transformers, and streaming/infra systems used in production. They will own impactful projects, contribute to systems that operate at significant scale, and collaborate with a highly experienced ML team. It’s an ideal opportunity for students who want to go beyond notebooks and gain true ML systems experience, with a strong pathway to full-time roles.

What Success Looks Like

By the end of the internship, the intern will have delivered an end-to-end ML project — from data exploration and feature engineering to building pipelines and running experiments. They will write clean, maintainable code, communicate their results clearly, and take ownership of their work. Success means their work can be used or extended in production and they can effectively collaborate in a fast-paced ML engineering team.

Compensation: $30-$32/hour USD