Hiring Ads Engineers: The Complete Guide

# Ads Engineer

**Location:** San Francisco, CA (Hybrid) · **Employment Type:** Full-time · **Level:** Mid-Senior

## About [Company]

[Company] is a retail media platform helping brands reach shoppers at the point of purchase. Our ads platform processes 10 billion+ ad requests daily, connecting brands with high-intent shoppers across our network of retail partners. We're growing 100%+ YoY and becoming a major player in the $50B retail media market.

Ads engineering at [Company] combines the scale challenges of Big Tech advertising with the entrepreneurial pace of a growth company. Every millisecond of latency, every percentage point of prediction accuracy, translates directly to revenue.

**Why join [Company]?**

- Work on ads at meaningful scale—10B+ daily requests, $500M+ annual ad spend
- High-performance systems challenges (sub-50ms p99 latency requirements)
- ML for real-time prediction (CTR, conversion, bid optimization)
- Join a 25-person ads team within a 200-person engineering org
- Series D company, profitable unit economics, fast growth
- Competitive compensation with strong equity component

## The Role

We're looking for an Ads Engineer to help us scale and improve our advertising systems. You'll work on core ads infrastructure: ad serving, real-time bidding, prediction models, and measurement—the systems that make ads work.

This is high-performance systems engineering combined with ML. Every ad request goes through your systems, and you have milliseconds to retrieve candidates, score them, run an auction, and return a result. Your prediction models determine which ads show, and your measurement systems tell advertisers whether their campaigns worked.

The ideal candidate has experience with high-scale, low-latency systems and understands the unique challenges of advertising: auction theory, prediction under constraints, and measurement in a privacy-focused world.

## What You'll Work On

### First 30 Days
- Onboard to our ads stack, serving infrastructure, and ML systems
- Understand our auction mechanics, targeting, and measurement
- Ship your first improvement (latency optimization, model feature, bug fix)

### First 90 Days
- Own a component of the ads stack (serving, prediction, or measurement)
- Contribute to model improvements or infrastructure optimization
- Understand advertiser needs and campaign mechanics
- Participate in on-call rotation for ads systems

### First Year
- Lead ads initiatives with direct revenue impact
- Architect improvements to serving, bidding, or measurement systems
- Develop new targeting or optimization capabilities
- Mentor teammates on ads engineering best practices

## Objectives of This Role

- Maintain sub-50ms p99 latency as traffic grows
- Improve prediction model accuracy (CTR, conversion)
- Scale ads serving to handle 2x traffic growth
- Build privacy-preserving measurement capabilities
- Improve advertiser ROI through better targeting and optimization

## Responsibilities

**Ad Serving & Infrastructure (50%)**
- Build and optimize real-time ad serving systems
- Design auction systems and bidding logic
- Implement candidate retrieval and ranking pipelines
- Optimize for latency and throughput at scale
- Build targeting and segmentation systems

**ML & Prediction (30%)**
- Develop prediction models (CTR, conversion probability)
- Build feature pipelines for real-time model serving
- Implement bid optimization and pacing algorithms
- Monitor model performance and investigate drift
- Collaborate with ML team on model improvements

**Measurement & Reporting (20%)**
- Design attribution and measurement systems
- Build reporting infrastructure for advertiser dashboards
- Implement privacy-preserving measurement approaches
- Create data pipelines for ads analytics
- Support advertiser integrations and APIs

## Required Skills and Qualifications

**Technical Skills**
- 5+ years of backend engineering experience
- 2+ years in advertising technology or similar high-scale systems
- Strong distributed systems experience (microservices, message queues)
- Experience with real-time, low-latency systems (sub-100ms)
- ML experience for prediction models (classification, ranking)
- Proficiency in Go, Java, or C++ for performance-critical systems
- Strong SQL and data pipeline skills

**Domain Knowledge**
- Understanding of auction mechanics and bidding strategies
- Familiarity with advertising concepts (CPM, CPC, ROAS)
- Experience with experimentation and A/B testing

**Soft Skills**
- Analytical mindset for performance optimization
- Collaboration with product, ML, and data teams
- Comfort with ambiguity in a fast-moving market

## Preferred Skills and Qualifications

- Retail media or e-commerce advertising experience
- Experience with privacy-preserving measurement (differential privacy, aggregated reporting)
- Bid optimization and budget pacing experience
- Real-time ML serving experience (< 10ms inference)
- Experience with ads-specific protocols (OpenRTB, VAST)
- Background in computational advertising research

## Tech Stack

**Serving:** Go for low-latency serving, custom auction engine

**ML:** Python, TensorFlow/PyTorch, custom feature store

**Data:** Apache Kafka, Spark, BigQuery, Airflow

**Infrastructure:** Kubernetes, Istio, custom load balancing

**Measurement:** Custom attribution system, privacy-preserving aggregation

**Monitoring:** Datadog, custom ads dashboards, PagerDuty

## Compensation and Benefits

**Salary:** $160,000 - $220,000 (based on experience)

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

**Benefits:**

*Health & Wellness*
- Medical, dental, and vision insurance (100% covered)
- $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 (AdExchanger, Programmatic I/O)
- Internal ads tech talks and knowledge sharing

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

*Workspace*
- $1,500 home office setup allowance
- Hybrid work (2-3 days in SF office)
- High-performance development machines

## Engineering Culture

**How we work:**
- Performance obsession—every millisecond matters
- Data-driven decisions—measure everything
- Move fast but maintain reliability
- Close collaboration between engineering and product
- On-call with proper support and escalation

**What we value:**
- System reliability—ads must serve
- Performance at scale—latency is revenue
- Advertiser success—their ROI is our success
- Privacy-first approach to measurement
- Continuous learning in a fast-evolving industry

## 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)
  Systems and ads fundamentals with an ads engineer.

- **Step 3: System Design** (90 min)
  Design an ads serving or measurement system. Focus on scale, latency, and tradeoffs.

- **Step 4: Coding Exercise** (90 min)
  Performance-focused coding. Algorithm optimization, data processing, or system implementation.

- **Step 5: ML Discussion** (60 min)
  Discussion about prediction models, feature engineering, and ML for ads.

- **Step 6: Team Interviews** (2 x 30 min)
  Meet potential teammates. Discussion about collaboration and working style.

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*[Company] is an equal opportunity employer.*