Distributed Training Engineer
Impact: Training speed and cost efficiency enabling larger and more capable AI models
Build and optimise the infrastructure and software systems that enable training of large neural networks across hundreds or thousands of GPUs. Design parallelism strategies, gradient communication protocols, and fault-tolerant training pipelines.
What the day looks like
- People interaction
- Minimal
- Team vs solo
- 45% Team / 55% Solo
- Client facing
- Rarely
- Impact visibility
- High
- Travel
- 5 to 10% for data centre visits
- Schedule flexibility
- Moderate
- Remote work
- Mostly Remote
- Typical work hours
- 45 to 60 hours/week
- Stress level
- High
At a glance
- Median salary
- $185,000
- Entry-level
- $130,000 - $160,000
- Senior
- $250,000+
- Growth by 2033
- 35% (much faster than average)
- Demand
- Growing Fast
- Freelance potential
- Very Low
- Salary growth potential
- High - 65 to 90% growth from entry to senior
- Typical student debt
- $20,000 - $60,000
Skills you'll use
Hard skills
- CUDA
- PyTorch
- NCCL
- Megatron-LM
- DeepSpeed
- Python
- C++
- Networking
Soft skills
- Problem-solving
- Attention to detail
- Systems thinking
- Intellectual curiosity
- 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
- 7 to 10 years
- Career switching
- Very Hard
Where this career leads
How people arrive here
Where you can go from here
Typical progression
- ML Engineer > Distributed Training Engineer > Senior Training Engineer > Staff Infrastructure Engineer > Principal Engineer
Future outlook
- Automation probability
- 5% extremely low risk as this role is at the hardware-software frontier
- AI disruption risk
- Very Low
- Demand trend
- Growing Fast
How people feel about it
- Overall satisfaction
- 8.3/10
- Meaning
- 8.5/10
- Work-life balance
- 6/10
- Prestige
- 8.5/10
- Social perception
- Very High