CUDA Kernel Engineer
Impact: Computational efficiency enabling faster and cheaper AI model training and inference
Write and optimise low-level GPU kernels in CUDA and Triton to maximise the computational efficiency of neural network operations. Implement custom attention mechanisms, quantisation routines, and memory-efficient operators that underpin frontier AI model performance.
What the day looks like
- People interaction
- Minimal
- Team vs solo
- 35% Team / 65% Solo
- Client facing
- Never
- 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
- $200,000
- Entry-level
- $140,000 - $170,000
- Senior
- $280,000+
- Growth by 2033
- 30% (much faster than average)
- Demand
- Growing Fast
- Freelance potential
- Very Low
- Salary growth potential
- Very high - 80 to 100% growth from entry to senior
- Typical student debt
- $20,000 - $60,000
Skills you'll use
Hard skills
- CUDA
- Triton
- C++
- Python
- GPU architecture
- FlashAttention
- Profiling tools
Soft skills
- Attention to detail
- Problem-solving
- Intellectual curiosity
- Persistence
- Systems thinking
Technical complexity: Very High
How to get there
- Minimum education
- Bachelor's Degree
- Licensing
- No
- Years to mid-career
- 4 to 6 years
- Years to senior
- 8 to 12 years
- Career switching
- Very Hard
Where this career leads
How people arrive here
Where you can go from here
Typical progression
- Systems Engineer > CUDA Engineer > Senior CUDA Engineer > Staff GPU Kernel Engineer > Principal Engineer
Future outlook
- Automation probability
- 3% near-zero risk as this is the most technically demanding role in AI infrastructure
- AI disruption risk
- Very Low
- Demand trend
- Growing Fast
How people feel about it
- Overall satisfaction
- 8.5/10
- Meaning
- 8.8/10
- Work-life balance
- 6.5/10
- Prestige
- 8.8/10
- Social perception
- Very High