Hiring Search Engineers: The Complete Guide

# Search Engineer

**Location:** Remote (US) · **Employment Type:** Full-time · **Level:** Mid-Senior

## About [Company]

[Company] is the leading marketplace for professional services, connecting millions of customers with the right professionals for their needs. Search is at the heart of our product—it's how customers discover professionals, how professionals get found, and how we generate revenue.

Our platform serves 15 million monthly active users, processes 50 million searches per month, and powers a $2B+ GMV marketplace. Great search directly drives our business success.

**Why join [Company]?**

- Own search for a product with real scale—50M+ monthly searches
- Work on both retrieval and ranking challenges
- Join a dedicated 8-person search team within a 200-person engineering org
- Series D company with strong unit economics and path to profitability
- Competitive compensation with meaningful equity
- Remote-first culture with optional offices in SF and NYC

## The Role

We're looking for a Search Engineer to help us build the next generation of our search and discovery platform. You'll work on the entire search stack: indexing pipelines, query understanding, ranking algorithms, and serving infrastructure.

The ideal candidate has deep search fundamentals combined with production experience. You understand inverted indices, BM25, and learning-to-rank, but you also know how to operate search systems reliably at scale. You're excited about the intersection of search and ML—semantic search, embedding models, and hybrid retrieval.

## What You'll Work On

### First 30 Days
- Onboard to our Elasticsearch cluster, indexing pipelines, and ranking systems
- Ship your first search improvement (query expansion, ranking tweak, or latency fix)
- Understand our search metrics and how we measure success

### First 90 Days
- Own a major search component (query understanding, ranking, or indexing)
- Run your first A/B test to validate a search improvement
- Contribute to our relevance evaluation framework
- Collaborate with product on search feature roadmap

### First Year
- Lead search initiatives that measurably improve conversion
- Architect our semantic search capabilities
- Mentor teammates on search best practices
- Shape our search technical roadmap

## Objectives of This Role

- Improve search relevance (measured by NDCG and conversion rate)
- Reduce search latency (p50 < 50ms, p99 < 200ms)
- Build semantic search capabilities for better intent matching
- Improve query understanding (autocomplete, spell correction, intent detection)
- Create evaluation frameworks that catch regressions before production

## Responsibilities

**Search Development (60%)**
- Build and optimize our Elasticsearch-based search platform
- Implement and tune ranking algorithms (BM25, learning-to-rank, hybrid)
- Build query understanding features (autocomplete, spell correction, synonyms)
- Develop semantic search using embedding models and vector search
- Optimize indexing pipelines for freshness and completeness

**Relevance & Experimentation (25%)**
- Design and run A/B experiments for search improvements
- Build evaluation frameworks using relevance metrics (NDCG, MRR, precision/recall)
- Analyze search logs to identify improvement opportunities
- Create relevance judgment guidelines and quality processes
- Monitor production search quality and investigate issues

**Infrastructure & Collaboration (15%)**
- Maintain search infrastructure reliability and performance
- Collaborate with ML team on learning-to-rank and embedding models
- Work with Product to understand user needs and prioritize features
- Document search systems and contribute to team knowledge base
- Participate in on-call rotation for search system issues

## Required Skills and Qualifications

**Technical Skills**
- 4+ years of search engineering experience in production environments
- Strong experience with Elasticsearch, Solr, or similar search platforms
- Deep understanding of information retrieval fundamentals (inverted indices, TF-IDF, BM25)
- Experience with search relevance metrics and evaluation methodology
- Production experience operating search at scale (millions of queries)
- Proficiency in Python or Java for search development
- Experience with A/B testing and experimentation

**Soft Skills**
- Strong analytical and problem-solving skills
- Ability to explain search concepts to non-technical stakeholders
- Collaborative approach with product and ML teams
- Self-directed with ability to own search features end-to-end

## Preferred Skills and Qualifications

- Learning-to-rank experience (XGBoost, LightGBM, neural rankers)
- Semantic search experience (embeddings, vector databases, hybrid retrieval)
- Marketplace or e-commerce search experience
- Experience with query understanding systems
- Familiarity with search analytics and user behavior analysis
- Background in information retrieval research

## Tech Stack

**Search Platform:** Elasticsearch 8.x cluster (500M+ documents)

**Indexing:** Apache Kafka for streaming, Airflow for batch pipelines

**Ranking:** Elasticsearch LTR plugin, custom Python ranking services

**Semantic Search:** Pinecone for vector search, Sentence Transformers for embeddings

**Experimentation:** Internal A/B platform, custom relevance evaluation tools

**Languages:** Python (ranking, ML), Java (Elasticsearch plugins), SQL (analytics)

**Infrastructure:** AWS (EC2, EKS), Terraform, Datadog for monitoring

## Compensation and Benefits

**Salary:** $150,000 - $200,000 (based on experience and location)

**Equity:** 0.05% - 0.15% (4-year vest, 1-year cliff)

**Benefits:**

*Health & Wellness*
- Medical, dental, and vision insurance (100% covered for employees)
- $100/month wellness stipend
- Mental health support through Modern Health

*Time Off*
- Unlimited PTO with 15-day minimum encouraged
- 11 paid company holidays
- 16 weeks paid parental leave

*Professional Development*
- $2,500 annual learning budget
- Conference attendance (Haystack, Activate, RecSys)
- Internal tech talks and knowledge sharing

*Financial*
- 401(k) with 4% company match
- Equity refresh grants annually

*Workspace*
- $1,500 home office setup allowance
- Remote-first (optional SF/NYC offices)
- Latest MacBook Pro and equipment

## Engineering Culture

**How we work:**
- Data-driven search improvements—every change measured
- A/B testing culture—ship with confidence
- Code review required for all changes
- Weekly search team syncs to review metrics
- Blameless post-mortems after incidents

**What we value:**
- Relevance obsession—search quality directly impacts customers
- Experimentation over intuition
- Understanding users through data
- Balancing innovation with reliability
- Remote collaboration done well

## Interview Process

Our interview process typically takes 2 weeks.

- **Step 1: Recruiter Screen** (30 min)
  Background discussion and role overview.

- **Step 2: Technical Screen** (60 min)
  Search fundamentals with a search engineer. We'll discuss IR concepts, your past search projects, and approach to common search problems.

- **Step 3: Search Deep Dive** (90 min)
  Technical discussion about search relevance, ranking, and evaluation. How would you improve search for a specific scenario?

- **Step 4: System Design** (90 min)
  Design a search system end-to-end: indexing, query processing, ranking, serving. Focus on your reasoning and tradeoffs.

- **Step 5: Team Interviews** (2 x 30 min)
  Meet potential teammates. Discussion about collaboration and working style.

- **Step 6: Hiring Manager** (30 min)
  Career goals and final questions.

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*[Company] is an equal opportunity employer.*