Senior Learning Design Lead

Handshake•San Francisco, CA

17h•$128,000 - $195,000•Remote

About The Position

As a Senior Learning Design Lead, you'll make quality compound across Handshake AI's portfolio of data projects. Reporting to the Head of Learning & Training, you'll work in two modes: embedded directly with Operations teams on fast launch cycles, and building the systems that carry quality judgment from one project to the next. Beyond individual projects, you'll bring a strong point of view to our design culture — pressure testing work, raising the bar, and acting as a thought partner to teammates on thorny design challenges and to the team lead on how our systems and standards should evolve. You'll also coach designers and Operators to grow their learning design and training craft, helping the whole team get better over time. This role is central to how the world's leading AI labs get the high-quality data they depend on.

Requirements

7+ years in instructional design or technical training, with a track record of translating complex technical concepts into effective learning
Practical AI fluency — you use these tools daily and can diagnose and redirect AI-generated content with pedagogical precision
Proven ability to ship training materials on tight deadlines in fast-paced environments where requirements change frequently
Experience driving quality standards across cross-functional teams without formal authority
Strong analytical instincts — you track quality metrics and completion rates to identify gaps and iterate on programs

Nice To Haves

Background in sales enablement or technical consulting, where you've rapidly upskilled diverse audiences on complex subject matter
Experience in AI/ML data operations or human data labeling workflows
Track record of building design review systems or quality rubrics that scale across teams

Responsibilities

Forward Deploy: Embed with Operations teams launching or scaling live projects, shipping targeted training materials within 24–48 hours to hit Fellow quality and throughput goals
Learning Design: Design and deploy playbooks, courses, and assessments that translate complex data annotation requirements into learner-ready experiences
Knowledge Transfer: Work with research and project teams to apply insights from individual projects across similar data types at scale
Design Review: Strengthen team quality through structured critique that holds a consistent bar across all learning deliverables
Operator Enablement: Train Operators on instructional design principles, feedback techniques, and quality assessment methods to build lasting coaching capabilities
Cross-Functional Alignment: Drive quality initiatives that align data science, operations, and product partners around a shared quality bar — without formal authority