Deep Learning Engineer
Impact: Core product capability and competitive moat through model performance
Design, train, and optimize deep neural networks for production applications including computer vision, NLP, and multimodal systems. Translate research papers into working code and scale models from prototype to deployment.
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
- 45 to 55 hours/week
- Stress level
- High
At a glance
- Median salary
- $160,000
- Entry-level
- $115,000 - $140,000
- Senior
- $210,000+
- Growth by 2033
- 32% (much faster than average)
- Demand
- Growing Fast
- Freelance potential
- Low
- Salary growth potential
- High - 65 to 85% growth from entry to senior
- Typical student debt
- $20,000 - $60,000
Skills you'll use
Hard skills
- PyTorch
- CUDA
- Python
- Transformer architectures
- Distributed training
- Model optimization
- MLflow
Soft skills
- Problem-solving
- Attention to detail
- Intellectual curiosity
- Collaboration
- Persistence
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
- Hard
Where this career leads
How people arrive here
Where you can go from here
Typical progression
- ML Engineer > Deep Learning Engineer > Senior DL Engineer > Staff ML Engineer > Principal Engineer > ML Director
Future outlook
- Automation probability
- 10% very low risk as the role creates the automation
- AI disruption risk
- Low
- Demand trend
- Growing Fast
How people feel about it
- Overall satisfaction
- 8/10
- Meaning
- 8.2/10
- Work-life balance
- 6.5/10
- Prestige
- 8.3/10
- Social perception
- Very High