Sr. Staff Engineer, AI Platform at quizlet-2

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 Learning Join 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 teams move materially faster from idea to experiment to production • A consistent MLflow-centered lifecycle exists for training, lineage, registry, promotion, rollback, and auditability • Quizlet has a durable foundation for both predictive ML and LLM serving, with stronger latency, safety, reliability, and cost controls • Platform adoption grows because the developer experience is simpler, more reliable, and clearly better than ad hoc solutions • The company has a clear technical north star for AI platform investments across data, training, inference, evaluation, and governance What you bring to the table: • 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 Bonus points if you have: • 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 Compensation, Benefits & Perks: • Quizlet is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. Salary transparency helps to mitigate unfair hiring practices when it comes to discrimination and pay gaps. Total compensation for this role is market competitive, including a starting base salary of $245,000 - $320,000, depending on location and experience, as well as company stock options • Collaborate with your manager and team to create a healthy work-life balance • 20 vacation days that we expect you to take! • 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 Why Join Quizlet?  🌎 Massive reach: 60M+ users, 1B+ interactions per week  🧠 Cutting-edge tech: Generative AI, adaptive learning, cognitive science  📈 Strong momentum: Top-tier investors, sustainable business, real traction  🎯 Mission-first: Work that makes a difference in people’s lives  🤝 Inclusive culture: Committed to equity, diversity, and belonging We strive to make everyone feel comfortable and welcome! We work to create a holistic interview process, where both Quizlet and candidates have an opportunity to view what it would be like to work together, in exploring a mutually beneficial partnership. We provide a transparent setting that gives a comprehensive view of who we are!   In Closing: At Quizlet, we’re excited about passionate people joining our team—even if you don’t check every box on the requirements list. We value unique perspectives and believe everyone has something meaningful to contribute. Our culture is all about taking initiative, learning through challenges, and striving for high-quality work while staying curious and open to new ideas. We believe in honest, respectful communication, thoughtful collaboration, and creating a supportive space where everyone can grow and succeed together.” Quizlet’s success as an online learning community depends on a strong commitment to diversity, equity, and inclusion.  As an equal opportunity employer and a tech company committed to societal change, we welcome applicants from all backgrounds. Women, people of color, members of the LGBTQ+ community, individuals with disabilities, and veterans are strongly encouraged to apply. Come join us! To All Recruiters and Placement Agencies: At this time, Quizlet does not accept unsolicited agency resumes and/or profiles.  Please do not forward unsolicited agency resumes to our website or to any Quizlet employee. Quizlet will not pay fees to any third-party agency or firm nor will it be responsible for any agency fees associated with unsolicited resumes. All unsolicited resumes received will be considered the property of Quizlet. #LI-onsite