Sr Backend Engineer
About The Position
Quizlet is seeking a Sr Backend Engineer to join their Search team. This role involves owning the end-to-end process of content indexing, search query services, and serving results with high relevance and low latency. The engineer will work at the intersection of data infrastructure, backend services, and search, focusing on data ingestion, Elasticsearch index design, retrieval services, and integrating embedding generation. The position requires collaboration with product and applied ML teams to evolve search capabilities towards multi-stage ranking and learning-to-rank systems. Responsibilities include designing and maintaining data pipelines, owning index design, building and operating hybrid retrieval infrastructure, designing backend retrieval/query services, integrating embedding generation, partnering with ML teams, monitoring and troubleshooting cluster health, implementing zero-downtime reindexing, establishing data quality practices, collaborating on cluster sizing and cost optimization, supporting experiment rollout, and staying current with retrieval infrastructure best practices.
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 Haves
- 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.
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.
Benefits
- 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
- Wellness benefits
- 40 hours of annual paid time off to participate in volunteer programs of choice
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
