Senior Backend Engineer - Data Pipelines & Vector Search

Quizlet; San Francisco (CA); Hybrid; USD 167,000 - 219,000; Full time

Benefits offered by this job

Health insurance

20 vacation days

401k with company match

LinkedIn Learning

Paid Family Leave

Commuter benefits

Wellness benefits

Job summary

Quizlet is seeking a Backend Engineer for the Search team to own end-to-end data pipelines, index design, and hybrid retrieval using lexical and vector search in Elasticsearch/OpenSearch. You will build scalable backend services, design APIs, and ensure low-latency results for a highly used learning platform.

You will work in-office at least two days a week (Wednesday and Thursday) and collaborate with product and ML teams to evolve from retrieval to multi-stage ranking, while prioritizing

Qualifications

• 4+ years of backend or data engineering with production pipelines.
• Strong SQL and data warehouse experience (Snowflake/BigQuery/Redshift).
• Python (or Java/Scala) for pipelines and services; orchestration tools experience.

Responsibilities

• Design, build, and maintain data pipelines ingesting content into Elasticsearch indices.
• Own index design, mappings, ILM, sharding, and lifecycle management.
• Build infrastructure for hybrid retrieval (lexical + vector) in Elasticsearch/OpenSearch.
• Develop backend retrieval services, APIs, caching, and routing.
• Integrate embeddings in pipelines and manage re-embedding when content changes.
• Collaborate with product/ML teams to evolve retrieval toward multi‑stage ranking.
• Monitor cluster health, latency, throughput, and implement zero‑downtime reindexing.

Skills

Backend engineering

SQL

Python

Elasticsearch/OpenSearch

Airflow/Dagster/Prefect

Docker/Kubernetes

REST/gRPC APIs

Data pipelines

Collaboration with ML

Education

Bachelor's degree in CS or related field

Tools

Elasticsearch/OpenSearch

Airflow

Dagster

Prefect

dbt

Snowflake/BigQuery/Redshift

Docker

Kubernetes

Job description

Quizlet is seeking a Backend Engineer for the Search team to own end-to-end data pipelines, index design, and hybrid retrieval using lexical and vector search in Elasticsearch/OpenSearch. You will build scalable backend services, design APIs, and ensure low-latency results for a highly used learning platform.

You will work in-office at least two days a week (Wednesday and Thursday) and collaborate with product and ML teams to evolve from retrieval to multi-stage ranking, while prioritizing