Backend Software Engineer (Distributed Systems / Python)
Glint Tech Solutions LLC · San Francisco, California, United States · $170K–$250K/yr
IT Services and IT Consulting · 11-50 employees
About the role
Design and maintain scalable backend systems and distributed services for real-time GPU allocation and resource scheduling. Own the full project lifecycle from technical design through deployment, monitoring, and operational maintenance.
What they look for
Requirements
Requires 3+ years of professional backend experience (5-8 preferred) with strong expertise in Python and production distributed systems. Must be proficient with Kubernetes, Docker, cloud platforms (AWS/GCP), and relational databases.
Benefits
Full description
About the Company
We are building the next generation of AI infrastructure by making GPU computing more flexible, efficient, and accessible. Our platform enables customers to consume GPU capacity through real-time marketplaces, spot bidding, and flexible reservations—bringing cloud-scale infrastructure together with market-driven resource allocation.
We're growing rapidly and are looking for a talented Backend Software Engineer to help build the distributed systems that power our platform.
Location
- Palo Alto / San Francisco, CA
- Hybrid/Onsite (4 days per week)
- Team is onsite in Palo Alto every Monday
Compensation
- $170,000 – $230,000 base salary
- Exceptional candidates may be considered up to $250,000
- Competitive equity package
- Comprehensive benefits
What You'll Do
- Design, build, and maintain highly scalable backend systems supporting real-time GPU allocation.
- Develop and evolve distributed services responsible for resource scheduling, reservation management, and capacity allocation.
- Build reliable backend infrastructure capable of handling concurrency, asynchronous workflows, and race conditions.
- Design and optimize pricing, allocation, and orchestration logic for large-scale distributed systems.
- Partner closely with cross-functional engineering teams to ensure efficient infrastructure utilization.
- Own projects from technical design through implementation, deployment, monitoring, and long-term maintenance.
- Improve platform reliability, resiliency, observability, and operational excellence.
- Build and maintain CI/CD pipelines, cloud infrastructure, and deployment automation.
- Participate in production on-call rotations and continuously improve operational tooling and runbooks.
Required Qualifications
- 3+ years of professional backend software engineering experience (5–8 years preferred).
- Strong expertise building and maintaining production distributed systems at scale.
- Proven experience handling:• Concurrency
- Race conditions
- Asynchronous request processing
- 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.
Preferred Qualifications
- 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.
Ideal Candidate
We're looking for someone who:
- Enjoys solving complex distributed systems problems.
- Has built and maintained production systems—not just prototypes or MVPs.
- Takes ownership from design through long-term operational support.
- Is comfortable working in a fast-paced startup environment.
- Can influence architecture while remaining highly hands-on.
- Thrives in collaborative, cross-functional engineering teams.
Technical Environment
- Python
- PostgreSQL
- Kubernetes
- Docker
- AWS
- GCP
- Terraform
- CI/CD
- Distributed Systems
- Cloud Infrastructure
