Gain a deep understanding of the three pillars of observability (logs, metrics, traces) and how they are used to monitor complex systems.
Help with validating, documenting, and exploring new data sources as they are onboarded into our observability platforms.
Assist senior engineers in building and refining dashboards that provide clear insights into system health and performance.
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.
Learn how AI/LLM applications are monitored by assisting with the instrumentation and analysis of telemetry from tools like Langfuse.
Help write and refine simple scripts (primarily in Python) to automate data quality checks or reporting tasks.
Help create and update documentation for tools, processes, and team projects.

Requirements

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.

Benefits

competitive compensation
Health and Wellness programs
Income Protection
Paid Leave
Retirement

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

Pythondata analysismachine learningdata preparationfeature explorationmodel creationmodel implementationmodel deploymentautomation scriptingdata quality checks

Soft Skills

proactive attitudeproblem-solving skillscollaborative workcommunication skillsdesire to learn