# Staff Software Engineer, Search

**Company:** [Zocdoc](https://hotfix.jobs/companies/zocdoc)
**Location:** New York, NY
**Role:** Backend Engineering
**Salary:** $211k – $285k/yr
**Skills:** C#, .Net, AWS, ECS, AWS Lambda, DynamoDB, SQS, Sns, Kinesis, Opensearch, S3, Vector Databases, Embeddings, Ml Model Serving, Distributed Systems
**Posted:** 2026-05-19

> Staff Software Engineer on the Search Services team leading design and development of high-performance backend services, APIs, and data infrastructure to power an intelligent, intent-driven search experience using embeddings, LLMs, and modern ranking models.

## Job Description

## Responsibilities
- Architect and build core backend services, APIs, and data infrastructure that power Zocdoc’s next-generation search platform
- Redesign how provider, availability, and content data is ingested and flows through indexing pipelines into search and ML systems
- Design and optimize low-latency query paths across tier-1 search service, vector search infrastructure, and cross-encoder ranking systems
- Integrate ML model serving, embedding generation, and ranking signals into production backend services through scalable, observable patterns
- Drive architectural direction and set engineering standards across the Search Services organization through design docs, technical deep dives, and high-signal code reviews
- Mentor engineers on backend best practices, distributed systems design, API architecture, and technical decision-making
- Build robust observability into backend services—measuring, debugging, and improving performance, reliability, and data freshness across the entire search stack

## Requirements
- Proven track record of owning and scaling complex, high-traffic backend platforms with deep expertise in distributed systems design and long-term architectural thinking
- Deep hands-on expertise with C# / .NET or comparable backend frameworks, with a focus on performance optimization, concurrency, and service-oriented architecture
- Significant experience designing and operating large-scale data pipelines, ingestion systems, or stream-processing workflows
- Strong experience with AWS services and cloud-native infrastructure patterns — ECS, Lambda, DynamoDB, SQS/SNS, Kinesis, OpenSearch, S3, and similar
- Familiarity with search technologies, vector databases, embedding models, and ML model serving, with a strong understanding of how to operate these at production scale
- Experience designing systems for high availability, fault tolerance, and graceful degradation in latency-sensitive environments
- Strong communication skills that align engineering, product, data science, and infrastructure stakeholders around shared goals
- Passion for mentoring engineers, setting engineering best practices, and raising the overall technical quality bar

## Nice-to-Haves
- Experience with vector retrieval, cross-encoder reranking, real-time ingestion, and indexing pipelines
- Background integrating ML model serving, embeddings, and LLM-driven flows into production backend systems

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