Baseten
Software Engineer - Training Infrastructure
About this role
Baseten is seeking a Software Engineer to design and build scalable infrastructure systems for its ML training platform. You'll architect scheduling, storage, and networking systems while partnering with research engineers to deliver reliable, high-performance training solutions for AI companies.
What you'll do
- Design scalable infrastructure systems including scheduling, storage, and networking for ML training
- Partner with developers and research engineers to translate training requirements into technical solutions
- Architect a global training scheduler and reinforcement learning systems
- Drive reliability improvements and development velocity across the platform
- Make critical architectural decisions balancing performance and system reliability
- Lead technical discussions and mentor junior engineers on infrastructure best practices
What they're looking for
- Go programming (Python a plus)
- Kubernetes in production environments
- Distributed systems design and performance tuning
- Observability systems design
- AWS and GCP cloud platforms
- ML/AI workloads and MLOps platforms
- Distributed storage systems
- Workload orchestration platforms
Benefits
- Competitive compensation with meaningful equity
- 100% medical, dental, and vision insurance coverage for employee and dependents
- Flexible PTO policy with company-wide winter break
- Paid parental leave
- Fertility and family-building stipend through Carrot
- Company-facilitated 401(k)
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Baseten
Baseten builds an AI inference platform that enables companies to deploy and manage machine learning models in production at scale. The company is hiring software engineers, infrastructure specialists, and product engineers to develop observability systems, frontend experiences, reliability infrastructure, developer tools, and enterprise deployment solutions.
- Website
- baseten.com
Likely interview questions
- Walk us through your experience designing and operating Kubernetes clusters in production. What scaling challenges have you encountered and how did you solve them?
- Describe a complex distributed system you've built or maintained. How did you approach observability and debugging issues across multiple components?