Sr Backend Engineer

About Quizlet

At Quizlet, our mission is to help every learner achieve their outcomes in the most effective and delightful way. We’re a $1B+ learning platform used by two-thirds of U.S. high school students and half of college students, powering over 1 billion learning interactions each week. We blend cognitive science with machine learning to personalize and enhance the learning experience for students, professionals, and lifelong learners alike.

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

About The Team

The Search team (part of Coach & Orchestration) owns the full path from raw content to search results — the pipelines and infrastructure that get content indexed, the services that query it, and the systems that serve it with high relevance and low latency. We're looking for a Backend Engineer who can own this end-to-end: from data ingestion and Elasticsearch index design through the retrieval/query services that power search in production.

Responsibilities

• Design, build, and maintain data pipelines that ingest, transform, and load content into Elasticsearch indices at scale.
• Own index design — mappings, analyzers, sharding strategy, and lifecycle management — balancing indexing throughput, query latency, and storage cost.
• Build and operate the infrastructure for hybrid retrieval, combining lexical (BM25) search with dense vector similarity (kNN/HNSW) in Elasticsearch.
• Design and maintain the backend retrieval/query services that sit in front of Elasticsearch — API design, request routing, caching, and query fan-out.
• Integrate embedding generation into pipelines — batching, caching, and re-embedding workflows when models or content change — using off-the-shelf or hosted embedding models.
• Partner with product and applied ML teams to support the evolution from retrieval into multi-stage ranking, including feeding features to future learning-to-rank systems.
• Monitor and troubleshoot cluster health, service latency, indexing throughput, and query performance; drive improvements in reliability and observability.
• Implement zero-downtime reindexing and index cutover strategies (aliasing, blue/green indices) to support continuous schema and data evolution.
• Establish data quality and validation practices to catch pipeline failures and indexing issues before they reach production.
• Collaborate with infrastructure/platform teams on cluster sizing, service scaling, and cost optimization.
• Support experiment rollout for retrieval and ranking changes, working with feature flagging or A/B test infrastructure.
• Stay current on Elasticsearch/OpenSearch and retrieval-infrastructure best practices, evaluating what's worth adopting.

Requirements

• Minimum 4+ years of experience in backend or data engineering, with hands-on ownership of production data pipelines and/or backend services.
• Strong SQL and experience with data warehouses (Snowflake, BigQuery, Redshift, or similar).
• Proficiency in Python (or Java/Scala) for pipeline and service development, and experience with orchestration tools (Airflow, Dagster, Prefect, or similar).
• Experience with dbt for data transformation, modeling, and testing within the warehouse.
• Hands-on experience with Elasticsearch or OpenSearch in production — index design, mappings, ILM, sharding, and cluster tuning.
• Experience designing and operating backend services/APIs (REST or gRPC) — request handling, caching, and performance optimization for low-latency, read-heavy systems.
• Experience with service observability — tracing, metrics, and alerting (Datadog, Prometheus/Grafana, or similar).
• Comfort with containerization and deployment (Docker, Kubernetes) for production services.
• Clear, effective communication, with the ability to collaborate well with data scientists, ML engineers, and product partners.
• Comfort operating in cloud infrastructure (AWS/GCP/Azure), including cost and performance tradeoffs for search infrastructure.

Nice to Have

• Experience with batch and/or streaming data processing (Spark, Kafka, Flink, or similar).
• Practical experience with vector search — dense_vector fields, kNN/HNSW, and combining lexical and vector scores for hybrid retrieval.
• Working familiarity with embedding models (open-source or hosted/API-based) — generating, storing, versioning, and refreshing embeddings at scale.
• Understanding of retrieval evaluation basics (recall@k, NDCG, MRR).
• Experience with learning-to-rank libraries (e.g., LightGBM, XGBoost rankers) or exposure to reranking pipelines.
• Experience with reciprocal rank fusion (RRF) or other hybrid score-blending techniques.
• Familiarity with vector databases beyond Elasticsearch (FAISS, ScaNN, pgvector, etc.).
• Prior experience scaling search/retrieval infrastructure in a high-traffic consumer or enterprise product.

Schedule

To support collaboration, we ask employees to be in the office at least two days a week: Wednesday and Thursday.

Benefits

• 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

Pay

Compensation Range: $167K - $219K