Windborne Systems
Machine Learning Infrastructure Engineer
- Confirmed live in the last 24 hours
- $140k–$240k
- Mid level
- Full-time
- On-site · Palo Alto, California, United States
- Added 2 months ago
About this role
WindBorne Systems is seeking a Machine Learning Infrastructure Engineer to build and maintain reliable infrastructure for their cutting-edge weather forecasting AI models. This role will focus on streamlining the research-to-operations pipeline, ensuring scalability and stability of their systems, and managing data pipelines from diverse sources. You'll play a critical role in enabling the research team to focus on model development and delivering timely, accurate weather intelligence.
What you'll do
- Build and maintain research-to-operations pipelines with strict latency requirements.
- Evaluate cost/performance tradeoffs across cloud and on-prem compute resources.
- Develop data pipelines for training and real-time data, handling upstream delays and quality checks.
- Improve the reliability of distributed training runs with monitoring and auto-recovery.
- Manage and scale inference infrastructure to meet customer demands.
- Troubleshoot and resolve production issues across nodes.
What they're looking for
- PyTorch
- Docker
- Data Pipelines
- Production ML Systems
- Large Datasets
- GPU Clusters
- Job Schedulers
- Cloud Computing
Benefits
- 401(k)
- Health, Dental, and Vision Insurance
- Unlimited PTO
- Stock Option Plan
- Office Food & Beverages
- Hybrid/In-Person Work
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Windborne Systems
- Website
- windbornesystems.com
Likely interview questions
- Describe your experience building and maintaining production ML systems. What are some common challenges you’ve faced and how did you address them?
- How do you approach building data pipelines that can handle unreliable or incomplete upstream data?