Lead Instructor: Machine Learning Data Associate

Remote

Responsibilities

Lead Instructors at Correlation One are responsible for delivering high-quality, live, virtual instruction and partnering with company personnel to drive exceptional learning outcomes.
Prepare and lead virtual classroom sessions for a range of learners, which may vary in size from 20 to 8,000+.
Deliver instruction on skills tailored to Learners' needs and data labeling needs
Oversee the management of class time Q&A and monitor chat flow, and overall class energy and engagement dynamics Collaboration:
Collaborate closely with Correlation One operations personnel to ensure smooth program delivery and adherence to schedules.
Adjust the lesson pace and presentation to meet the needs of diverse learners while also maintaining responsibility for timely delivery of the prepared content.
Exhibit an energy, pacing, and ability to make complex topics accessible and maintain strong learner engagement Communication:
Maintain extra communicative contact with Correlation One personnel. Positive Attitude:
Foster a healthy learning environment by maintaining a positive attitude and promoting a culture of learning. Course Improvement:

We work with Fortune 500 enterprises, federal and state government agencies, and leading employers to close skills gaps in AI, data analytics, cybersecurity, and operations leadership.
Bilingual Proficiency required for Machine Learning Data Associate 1 program: Deliver all instruction and learner support in German; use English professionally to collaborate with the C1 team (written updates, alignment meetings, issue escalation, and clarifications).
Technical ML Knowledge: Strong working knowledge of the ML lifecycle — from data collection and model training to evaluation and deployment — sufficient to explain how labeling decisions affect model behavior and downstream outputs, without requiring learners to build models themselves.
Generative AI & Foundation Model Expertise: Practical understanding of how large language models and multimodal foundation models work, including pre-training, fine-tuning, RLHF, and the role of human feedback in model alignment — able to connect these concepts directly to the annotator's role.
Prompt Engineering Proficiency: Hands-on