Rebel Space

Machine Learning Engineer

Rebel Space

4 months ago
Long Beach, CA
Hybrid
Full Time
Senior
0 applicants
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Rebel Space
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About this role

A Machine Learning Engineer at Rebel Space Technologies is responsible for designing, implementing, and optimizing machine learning models for anomaly detection in satellite communications. This role involves developing portable ML infrastructure for deployment in cloud and edge environments, conducting research on ML architecture, and building data pipelines while ensuring compliance with government and defense standards. The ideal candidate has strong experience in scientific Python and modern ML frameworks, along with a proven background in data analytics and machine learning engineering.

Skills

Qualifications

Bachelor's degree in Computer Science, Electrical Engineer, Physics or related technical disciplinePhD, Masters, or equivalent in Computer Science, Electrical Engineer, Physics or related field5+ years of professional experience in machine learning engineering3+ years of relevant industry experience in data analytics and machine learningExperience with ML infrastructure tools (e.g. MLflow, Kubeflow, Airflow, feature stores, model registries)
Rebel Space

About Rebel Space

www.rebelspacetech.com

Rebel Space is a leading provider of integrated observability and cybersecurity solutions tailored for modern space operations. With a mission to safeguard critical defense and commercial space infrastructure, the company leverages AI-driven technologies to enhance operational visibility and facilitate proactive threat monitoring and response. Its flagship platforms, Stargazer and Muse, enable operators to detect electronic and cyber threats in real-time, optimizing the management of autonomous air and space systems while ensuring mission readiness. Rebel Space is committed to empowering space operators through advanced solutions designed for dynamic and contested environments.

About Rebel Space

Headquarters

San Francisco, CA

Company Size

201-500 employees

Founded

2018

Industry

Technology

Glassdoor Rating

4.2 / 5

Leadership Team

Sarah Johnson

Chief Executive Officer

Michael Chen

Chief Technology Officer

Emily Williams

VP of Engineering

David Rodriguez

VP of Product

Jessica Thompson

Chief Financial Officer

Andrew Park

VP of Sales

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