Ranking Engineer
Impact: User engagement and revenue through optimised content and product ordering
Build and optimise machine learning ranking systems for feeds, search results, ads, and content recommendations. Design learning-to-rank models, feature pipelines, and online evaluation frameworks that directly drive user engagement and revenue.
What the day looks like
- People interaction
- Moderate
- Team vs solo
- 55% Team / 45% Solo
- Client facing
- Rarely
- Impact visibility
- High
- Travel
- Minimal
- Schedule flexibility
- Flexible
- Remote work
- Mostly Remote
- Typical work hours
- 40 to 50 hours/week
- Stress level
- Moderate
At a glance
- Median salary
- $160,000
- Entry-level
- $115,000 - $140,000
- Senior
- $210,000+
- Growth by 2033
- 20% (faster than average)
- Demand
- Growing Fast
- Freelance potential
- Low
- Salary growth potential
- High - 65 to 80% growth from entry to senior
- Typical student debt
- $20,000 - $60,000
Skills you'll use
Hard skills
- Python
- XGBoost
- LightGBM
- TensorFlow
- A/B testing
- Feature engineering
- Spark
Soft skills
- Analytical thinking
- Problem-solving
- Data-driven decision making
- Collaboration
- Attention to detail
Technical complexity: Very High
How to get there
- Minimum education
- Bachelor's Degree
- Licensing
- No
- Years to mid-career
- 3 to 5 years
- Years to senior
- 6 to 9 years
- Career switching
- Moderate
Where this career leads
How people arrive here
Where you can go from here
Typical progression
- ML Engineer > Ranking Engineer > Senior Ranking Engineer > Staff ML Engineer > Principal Engineer
Future outlook
- Automation probability
- 15% low risk as business objective alignment and experiment design require human judgment
- AI disruption risk
- Moderate
- Demand trend
- Growing Fast
How people feel about it
- Overall satisfaction
- 7.9/10
- Meaning
- 7.8/10
- Work-life balance
- 7/10
- Prestige
- 7.9/10
- Social perception
- High