Leidos is seeking a motivated and curious AI/ML Observability Intern to join our Enterprise Observability Team within the Chief Data & Analytics Office (CDAO). This is a 100% remote internship opportunity for a student passionate about the intersection of Artificial Intelligence, Machine Learning, and large-scale systems engineering.

This role provides a unique opportunity to gain hands-on experience with enterprise-grade observability platforms and learn how AI/ML is applied to massive telemetry datasets (logs, metrics, traces) to improve system reliability, performance, and business outcomes. You will work alongside senior engineers and data scientists on meaningful projects that contribute directly to our AIOps and Agentic AI initiatives.

Position Summary:
As an AI/ML Observability Intern, you will learn the fundamentals of modern observability and assist the team in its mission to turn data into actionable insights. You will gain exposure to industry-leading tools like Splunk, Cribl, Datadog, SolarWinds, and Langfuse. Under the guidance of a mentor, you will contribute to projects involving data analysis, visualization, automation, and the AI/ML lifecycle.

Primary Responsibilities:

• Learn Observability Fundamentals: Gain a deep understanding of the three pillars of observability (logs, metrics, traces) and how they are used to monitor complex systems.
• Assist with Telemetry Data Tasks: Help with validating, documenting, and exploring new data sources as they are onboarded into our observability platforms.
• Contribute to Dashboards & Visualization: Assist senior engineers in building and refining dashboards that provide clear insights into system health and performance.
• Support AI/ML Model Lifecycle: Assist data scientists with tasks related to model creation, implementation, and deployment, such as data preparation, feature exploration, and validating model results for observability use cases.
• Explore AI Observability: Learn how AI/LLM applications are monitored by assisting with the instrumentation and analysis of telemetry from tools like Langfuse.
• Contribute to Automation: Help write and refine simple scripts (primarily in Python) to automate data quality checks or reporting tasks.
• Contribute to Documentation: Help create and update documentation for tools, processes, and team projects.

Basic Qualifications:

• Currently enrolled in an accredited Bachelor’s or Master’s degree program in Computer Science, Data Science, Statistics, Engineering, or a related quantitative field.
• US citizenship required.
• Basic programming or scripting knowledge (Python is strongly preferred).
• Academic or project-based exposure to data analysis or machine learning concepts.
• A strong desire to learn, a proactive attitude, and excellent problem-solving skills.
• Ability to work collaboratively in a remote team environment.

Preferred Qualifications:

• Coursework or projects related to machine learning, statistical modeling, or AI.
• Familiarity with data formats like JSON or CSV.
• Exposure to cloud platforms (e.g., AWS, Azure) or Linux/Unix environments.
• Familiarity with version control using Git.

If you're looking for comfort, keep scrolling. At Leidos, we outthink, outbuild, and outpace the status quo — because the mission demands it. We're not hiring followers. We're recruiting the ones who disrupt, provoke, and refuse to fail. Step 10 is ancient history. We're already at step 30 — and moving faster than anyone else dares.

Original Posting:

For U.S. Positions: While subject to change based on business needs, Leidos reasonably anticipates that this job requisition will remain open for at least 3 days with an anticipated close date of no earlier than 3 days after the original posting date as listed above.

Pay Range:

The Leidos pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.