Staff AI ML Engineer

Who we are:

MasterClass is the streaming platform where the world’s best come together so anyone, anywhere, can access and be inspired by their knowledge and stories. We put you in the room with the creators, thinkers, makers and leaders who have changed the world, so that you can change yours.

Members get unprecedented access to 200+ instructors and classes across a wide variety of fields, including Arts & Entertainment, Business, Design & Style, Sports & Gaming, Writing and more. Step into Nas’ recording studio and Gordon Ramsay’s kitchen, and go behind the big screen with James Cameron. Design your career with Elaine Welteroth, get ready to win with Lewis Hamilton, perfect your pitch with Shonda Rhimes and discover your inner negotiator with Chris Voss.

We’re a remote-first workforce with collaborative work spaces in San Francisco and Kitchener, Ontario, and employees in several U.S. states. If you’re interested in joining a dynamic, culture-driving company—where learning invaluable skills is all in a day’s work—we invite you to apply.

Snapshot of the Role: MasterClass is building the next generation of learning products with AI at the core. Foundation models and agentic systems are central to how we design and deliver every new product, powering intelligent, adaptive experiences that help millions of people learn from the world's best. This is not a side initiative. It is the direction of the company.

We are looking for a Staff ML Engineer to join our AI engineering team and help define and deliver these products. This role is ultimately about delivery. You will be expected to stay deeply connected to the latest research, models, and techniques, but the measure of success is shipping products that people use. You will work alongside a small, talented group of ML engineers and partner closely with product, design, and engineering leadership to shape what AI-powered learning looks like at MasterClass.

What You Will Do:

• Design and build agentic AI systems that power consumer learning products, including multi-step reasoning, tool use, and orchestration architectures.
• Select and integrate foundation models (LLMs, multimodal) based on product requirements, making pragmatic trade-offs between quality, latency, cost, and safety.
• Build evaluation and observability frameworks that ensure AI product quality at scale, including automated testing, human-in-the-loop review, and production monitoring.
• Architect reliable, production-grade AI pipelines, including prompt management, context retrieval (RAG), caching, and fallback strategies.
• Collaborate with Product, Design, and Engineering to translate ambiguous product goals into well-defined technical approaches and ship features end-to-end.
• Stay deeply current with the foundation model landscape—new models, techniques, agentic frameworks, and research—and bring that knowledge to bear on product decisions.
• Ment