> Markdown version of [/jobs/ext/1343518-sr-backend-engineer](https://www.wearedevelopers.com/jobs/ext/1343518-sr-backend-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Sr Backend Engineer - **Company:** Quizlet, Inc. - **Location:** New York, NY, United States - **Experience:** Expert - **Salary:** $167,000.0 - $219,000.0 - **Contract:** Permanent contract - **Skills:** Query Performance, Java (Programming Language), Application Programming Interfaces (APIs), Airflow, Aliasing, Amazon Web Services, Microsoft Azure, BigQuery, Cloud Computing, Information Engineering, Data Infrastructure, Data Transformation, Data Warehousing, Shard (Database Architecture), Elasticsearch, Python (Programming Language), Open Source Technology, Operational Databases, Performance Tuning, Prometheus, Standard Sql, Search Technologies, Service Development Studio, Data Streaming, Management of Software Versions, Workflow Management Systems, Datadog, Large Language Models, Snowflake, Grafana, Apache Spark, Caching, Backend, Containerization, Kubernetes, Low Latency, Apache Flink, Xgboost, Apache Kafka, Api Design, Docker, Amazon Redshift - **Published:** July 19, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=a846c2cc34851159 ## About the Role * 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., * 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. ## Description You'll bring strong backend and data engineering fundamentals - pipeline design, orchestration, data modeling, and service/API development - with enough exposure to embeddings, vector search, and ML-adjacent concepts to support our hybrid (lexical + vector) retrieval today and our move toward ranking and relevance improvements tomorrow. You'll work at the intersection of data infrastructure, backend services, and search, ensuring our indices are fresh and our retrieval services are performant, reliable, and built to support increasingly sophisticated search., To support collaboration, we ask employees to be in the office at least two days a week: Wednesday and Thursday., * 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. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [How building an industry DBMS differs from building a research one](https://www.wearedevelopers.com/videos/768-how-building-an-industry-dbms-differs-from-building-a-research-one) - [Debugging in the Dark](https://www.wearedevelopers.com/videos/1658-debugging-in-the-dark) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) ## Related Articles - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [What’s the Difference Between Frontend and Backend Development?](https://www.wearedevelopers.com/magazine/240-what-s-the-difference-between-frontend-and-backend-development) - [Why Upskilling And Reskilling is Important For Developers](https://www.wearedevelopers.com/magazine/428-why-upskilling-and-reskilling-is-important-for-developers)