Backend Software Engineer (Distributed Systems / Python)
$170,000–$230,000 year
HybridSan Francisco, California, United States
Job Summary
Design, build, and maintain highly scalable backend systems supporting real-time GPU allocation, focusing on resource scheduling, reservation management, and capacity allocation. Develop distributed services that handle concurrency, asynchronous workflows, and race conditions while optimizing pricing, allocation, and orchestration logic. Own projects from technical design through implementation, deployment, monitoring, and long-term maintenance, including building CI/CD pipelines and cloud infrastructure on AWS and GCP. Participate in production on-call rotations to improve operational excellence, resiliency, and observability. Collaborate closely with cross-functional teams to ensure efficient infrastructure utilization within our fast-paced startup environment.
Required Qualifications
- 3+ years of professional backend software engineering experience
- Strong expertise building and maintaining production distributed systems at scale
- Proven experience handling concurrency
- Proven experience handling race conditions
- Proven experience handling asynchronous request processing
- Proven experience handling distributed system reliability
- Strong proficiency in Python
- Experience with PostgreSQL or other relational databases
- Hands-on experience with Kubernetes and Docker
- Experience deploying cloud-native applications on AWS and/or GCP
- Familiarity with Infrastructure as Code tools such as Terraform
- Strong understanding of software architecture, scalability, and production operations
- Experience participating in system design and architecture discussions
Desired Qualifications
- 5–8 years experience
- Experience at Big Tech or VC-backed startups
- Experience building marketplace platforms, auction engines, exchanges, reservation systems, billing platforms, or pricing engines
- Experience with observability, monitoring, and production incident response
- Strong understanding of distributed scheduling or orchestration systems
- Comfortable contributing across multiple areas of the technology stack
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