AI-First Learning Platform Lead

Cacheflow; United States; On-site; USD 171,000 - 236,000; Full time

Job summary

Databricks is seeking a Lead Learning Product Manager to own the platform learning experience for customers, partners, and internal practitioners. You’ll shape in‑product journeys, adaptive learning, and analytics to prove real skill development and adoption of the data/AI platform.

You will collaborate with engineering, UX, curriculum, and go‑to‑market teams to translate pedagogical goals into scalable capabilities, with a focus on predictive signals, dashboards, and at‑scale personalization.

Qualifications

• 5–8+ years of experience in product management, UX‑focused product roles, or learning/education platforms, ideally within B2B SaaS, developer tools, or data/AI products.
• Demonstrated experience owning a product area end to end: defining strategy and roadmap, writing PRDs, collaborating with engineering and design, and shipping impactful user‑facing features.
• Experience building or working on learning systems, training platforms, or content/knowledge experiences (LMS, certification, education analytics, in‑product guidance, or similar).
• Strong UX instincts and a track record of collaborating closely with designers and researchers to create intuitive flows, information architectures, and content experiences.
• Comfort working with data: defining metrics, instrumenting experiments, and using analytics to understand user behavior, learning outcomes, and skills gaps.
• Excellent written and verbal communication skills, with an ability to explain complex concepts simply and advocate for learners across the organization.
• Experience with AI‑powered learning, adaptive systems, or building products that incorporate LLMs into user workflows

Responsibilities

• Define and drive the product vision, strategy, and roadmap for AI‑first learning systems on the Databricks platform, anchored on contextual, in the flow of work enablement and a unified skills infrastructure.
• Design experiences that surface targeted knowledge precisely when users need it, triggered by real work context (e.g., usage patterns, errors, task types), reducing interruptions and friction.
• Translate learning and pedagogical goals into platform capabilities such as learner models, skills graphs, progress tracking, adaptive learning paths, assessments, and feedback loops.
• Partner with UX and content design to create intuitive, embedded guidance and learning patterns that feel like part of the workflow, not separate training destinations.
• Collaborate with platform and infrastructure teams to ensure learning and skills systems are reliable, secure, performant, and able to serve global enterprise customers at scale.
• Define and evolve predictive signals and analytics that give proactive visibility into emerging skills and capability gaps, enabling action before they impact business outcomes.
• Build features and tooling that allow enablement teams to deliver at‑scale personalization - leveraging AI and automation.
• Provide employees and leaders with transparent views into skills, enablement activities, and progress toward role and organizational goals through dashboards, reports, and in‑product insights.
• Work closely with education, documentation, and curriculum teams to empower them with self‑service configuration and experimentation tools for new learning experiences.
• Engage directly with customers, partners, and internal field teams to deeply understand learning needs across roles and transform these into clear product requirements.

Skills

Product management

UX-focused design

Learning systems

Data/AI products

End-to-end ownership

Job description

Databricks is seeking a Lead Learning Product Manager to own the platform learning experience for customers, partners, and internal practitioners. You’ll shape in‑product journeys, adaptive learning, and analytics to prove real skill development and adoption of the data/AI platform.

You will collaborate with engineering, UX, curriculum, and go‑to‑market teams to translate pedagogical goals into scalable capabilities, with a focus on predictive signals, dashboards, and at‑scale personalization.