Machine Learning Intern

Marble Technologies•Lincoln, NE

11h

About The Position

Marble is seeking a motivated and technically strong Machine Learning Intern to help build internal systems for summarizing and analyzing production data using large language models, statistical tools, and modern data infrastructure. This is a hands-on opportunity to work on applied AI systems that connect production data, analytics, and agent-based workflows in a real industrial setting. As a Machine Learning Intern at Marble, you will help build the AI and data infrastructure that allows LLM-powered agents to analyze packoff and production data at scale. This role is centered on applied AI systems and data engineering rather than traditional model training. You will develop pipelines that extract, process, and organize data from platforms such as ClickHouse and Spark, create statistical and analytical tools that LLMs can use through tool calls, and build orchestration logic for agents that identify trends, surface issues, and communicate insights. Depending on project needs, you may also support integrations with Slack for notifications, human-in-the-loop guidance, and agent supervision. This role is ideal for someone who is excited by applied AI, data engineering, distributed data systems, and building practical software that connects LLMs to real industrial workflows.

Requirements

Pursuing a Bachelor’s or Master’s degree in Computer Science
Foundational understanding of machine learning, data systems, or LLM-based applications
Experience with Python
Strong analytical and problem-solving skills.
Ability to work independently while collaborating across engineering teams
Strong written and verbal communication skills

Nice To Haves

Experience with LLM APIs, tool calling, agent workflows, or prompt engineering
Experience with cloud platforms such as AWS, GCP, or Azure
Familiarity with version control systems such as Git
Interest in industrial operations, manufacturing analytics, or applied AI systems

Responsibilities

Build pipelines to retrieve, process, and organize packoff and production data using ClickHouse, Spark, and related systems
Create statistical and analytical tools that LLM-based agents can use through tool calls
Design workflows for agents to summarize data, identify trends, and surface anomalies
Support orchestration of AI systems that combine data retrieval, tool use, and language-model reasoning
Help integrate agent workflows with Slack or similar tools for reporting, alerts, and human guidance
Partner with engineering teams to define KPIs, summaries, and operational monitoring outputs
Validate and improve the accuracy and reliability of LLM-generated outputs
Document pipelines, tools, prompts, and system workflows
Test, debug, and improve internal AI and data systems
Research new methods for agent tooling, analytics, and operational reporting