Sr. Staff Engineer, AI Platform

Quizlet • San Francisco, CA

1d • Onsite

About The Position

Join Quizlet to design and deliver AI-powered learning tools that scale across the world and unlock human potential. 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. 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.

Requirements

• 10+ years of experience building large-scale ML, data, or inference platforms in production environments
• Proven track record operating as a staff or senior-staff IC, setting technical direction across multiple teams or an entire engineering organization
• Deep expertise in distributed systems and cloud infrastructure, preferably on Google Cloud, including containerized workloads on Kubernetes and GKE
• Strong familiarity with MLflow for model training and inference workflows, including experiment tracking, reproducibility, model registry, packaging, promotion, and deployment patterns
• Experience building platforms for model training, feature management, dataset lineage, online and batch inference, and model observability
• Hands-on experience with LLM and GenAI infrastructure, such as model serving, retrieval-augmented generation, vector retrieval, evaluation frameworks, prompt and version management, and safety or quality guardrails
• Strong engineering skills in Python plus one or more backend or systems languages such as Go, Java, or Scala
• Strong judgment in system design, reliability, security, privacy, and cost-performance tradeoffs
• Exceptional communication and stakeholder leadership skills, with a record of mentoring senior engineers and driving alignment in ambiguous environments

Nice To Haves

• Experience with Ray, Vertex AI, BigQuery, Pub/Sub, Spark, Flink, dbt, feature stores, Triton, vLLM, or similar platform technologies
• Experience supporting ranking, retrieval, search, recommendation, personalization, or other consumer-facing ML systems
• Experience building evaluation and observability tooling for both predictive models and LLM-based systems
• Experience in EdTech, consumer learning products, or domains where trust, quality, and safety matter deeply

Responsibilities

• 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

Benefits

• Company stock options
• Healthy work-life balance
• 20 vacation days
• Competitive health, dental, and vision insurance (100% employee and 75% dependent PPO, Dental, VSP Choice)
• Employer-sponsored 401k plan with company match
• Access to LinkedIn Learning and other resources to support professional growth
• Paid Family Leave, FSA, HSA, Commuter benefits, and Wellness benefits
• 40 hours of annual paid time off to participate in volunteer programs of choice

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed
• Number of Employees: 251-500 employees