Sr. Staff Engineer, AI Platform

About Quizlet:
At Quizlet, our mission is to help every learner achieve their outcomes in the most effective and delightful way. Our $1B+ learning platform serves tens of millions of students every month,  including two-thirds of U.S. high schoolers and half of U.S. college students, powering over 2 billion learning interactions monthly.
We blend cognitive science with machine learning to personalize and enhance the learning experience for students, professionals, and lifelong learners alike. We’re energized by the potential to power more learners through multiple approaches and various tools.
Let’s Build the Future of LearningJoin us to design and deliver AI-powered learning tools that scale across the world and unlock human potential.

About the Team:

The AI & Data Platform team builds the foundation that powers applied AI across Quizlet: personalization and recommendations, retrieval and ranking, AI Coach, generative content, and emerging agentic experiences. We own the systems that make model development fast, reliable, observable, and safe, from data and features through training, evaluation, deployment, and inference.

This team is pragmatic about build versus buy. We aggressively use the right mix of managed Google Cloud services, best-in-class vendor tooling, open-source infrastructure, and internal platform abstractions when that gives Quizlet the best combination of speed, reliability, and leverage.

About the Role:

As a Senior Staff Engineer on the AI Platform team, you will define the technical direction for Quizlet’s next generation of ML and LLM infrastructure. This is a deeply hands-on, org-level individual contributor role. You will architect critical platform systems, drive build-versus-buy decisions, partner with leaders across Applied AI, Data Science, Product Engineering, and Infrastructure, and raise the bar for how models and LLM-powered systems are trained, evaluated, shipped, served, and governed across the company.

This role is ideal for an engineer who can operate at senior-staff scope in a large company, but wants the speed, ownership, and breadth of impact that come with a smaller, cloud-native environment. At Quizlet, the role spans the real stack rather than a narrow subsystem: Google Cloud, Kubernetes and GKE, distributed training, MLflow-centered workflows, data and feature foundations, online and asynchronous inference, and the evaluation and observability needed to run predictive ML and LLM systems safely at scale.

We’re happy to share that this is an onsite position in our San Francisco office. To help foster team collaboration, we require that employees be in the office a minimum of three days per week: Monday, Wednesday, and Thursday and as needed by your manager or the company. We believe this work environment enhances efficiency, fosters collaboration, and supports growth for both employees and the organization.

In this role, you will:

• Set the multi-year architecture and technical roadmap for Quizlet’s AI platform across data, features, model development, evaluation, deployment, and serving
• Standardize MLflow-based workflows for experiment tracking, model packaging, artifact lineage, model registry, promotion, rollback, and inference deployment patterns
• Build and evolve the training foundation for both batch and distributed workloads on Google Cloud, with strong reproducibility, dataset versioning, and clear contracts between code, data, and models
• Design and scale reliable model-serving infrastructure for both classic ML and GenAI workloads, including low-latency APIs, asynchronous inference, GPU-backed services, autoscaling, canary and shadow rollout patterns, rollback safety, and cost-performance optimization
• Define Quizlet’s LLM platform patterns, including model gateways, prompt and version management, caching, batching, traffic routing, retrieval-augmented generation, evaluation harnesses, and safety guardrails
• Drive training-serving consistency and strong online-offline contracts across data pipelines, feature definitions, model packaging, and serving interfaces
• Improve platform reliability and observability with clear SLOs for critical pipelines and model services, plus strong visibility into latency, freshness, availability, drift, and cost.
• Guide build-versus-buy decisions across Google Cloud services, vendor tooling, open-source components, and internal platform abstractions
• Partner closely with Applied AI, Data Science, Product, Security, and Infrastructure teams to turn platform investments into faster iteration, safer launches, and measurable learner and business impact
• Mentor senior engineers and act as a technical force multiplier across the organization
  
  

What Success Looks Like in 12 Months:

• AI