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# Software Engineer II

- **Company:** DigitalOcean, LLC
- **Location:** San Francisco, CA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $167,200.0 - $209,000.0
- **Contract:** Permanent contract
- **Skills:** Query Performance, Artificial Intelligence, Software as a Service, Relational Databases, Linux, Distributed Systems, PostgreSQL, MySQL, Platform as a Service (PAAS), Performance Tuning, Digitalocean, Software Engineering, Cloud Platform System, Model Inference
- **Published:** October 7, 2026
- **Apply:** https://startup.jobs/senior-software-engineer-ii-managed-databases-digitalocean-10320744

## About the Role

* Experience: 8+ years of professional software engineering experience building and operating large-scale distributed systems or data-plane/control-plane infrastructure (SaaS/PaaS or equivalent).
 * Production depth: Proven track record diagnosing and solving complex, high-concurrency engineering problems in production cloud environments.
 * Database platform fluency: Working knowledge of relational database engine internals-specifically PostgreSQL and/or MySQL-including query performance, replication, and scale (preferred).
 * Distributed systems mastery: Strong understanding of concurrency, failure modes, multi-region concerns, and performance optimization in production Linux/cloud environments.
 * Scale for data & AI workloads: Experience scaling services for compute, memory, and networking demands of data-intensive or AI inference-adjacent workloads (preferred).
 * Collaboration & mentorship: Track record of driving complex cross-team technical projects, mentoring engineers, and communicating tradeoffs clearly with partners and leadership.
 * Growth mindset: Appetite for feedback and a "leave it better than you found it" approach to architecture and maintainability.

## Description

* Architect and implement DBaaS platform systems: Design and build scalable control-plane and infrastructure components that improve throughput, multi-region operability, and reliability for managed database workloads.
 * Turn ambiguous requirements into deliverable architecture: Deconstruct customer and system needs into clear technical designs and workstreams that drive predictable delivery with product and engineering partners.
 * Own performance and resiliency outcomes on your services: Improve resource utilization, fault tolerance, and availability-using profiling and targeted changes against I/O, CPU, and network bottlenecks under real production load.
 * Strengthen lifecycle capabilities: Ship improvements to automated failover, continuous backups, point-in-time recovery (PITR), and maintenance workflows so clusters handle higher data volumes and tighter SLAs.
 * Scale for data-heavy and AI-adjacent demand: Expand and harden control-plane and infrastructure pieces that support compute-heavy and high-concurrency customer workloads.
 * Raise the team engineering bar: Guide technical decisions for engineers on the squad through design and code reviews; contribute clear design docs for the initiatives you lead.
 * Mentor team members: Coach engineers through technical guidance, architectural reviews, and code-quality standards to accelerate growth and execution velocity.
 * Operate what you build: Establish observability, failover, and recovery practices; participate in on-call; turn incident learnings into durable platform improvements.