Harrison Clarke · 10 hours ago
Senior Staff Software Engineer
Harrison Clarke is a high-growth, tier-1 VC-backed startup in the AI code generation space, hiring a Staff Distributed Systems Engineer to help design and build core systems for a next-generation AI product. This hands-on role involves developing low-latency services and scalable data orchestration while solving complex engineering problems.
ConsultingDevOpsHuman ResourcesInformation TechnologyStaffing Agency
Responsibilities
Design, build, and operate distributed systems that are reliable under real-world load and failure modes
Develop core backend services in Go and Python (service frameworks, orchestration, control planes, APIs)
Solve problems across consistency, concurrency, throughput, latency, resiliency, backpressure, and graceful degradation
Build systems for job scheduling / workload orchestration and efficient compute utilisation (including demanding AI workloads)
Improve observability and debugging for complex systems: tracing, metrics, structured logging, and profiling
Lead architectural decisions: data flows, service boundaries, state management, and scaling strategies
Set engineering standards and mentor others, while remaining deeply technical and hands-on
Qualification
Required
Strong experience building production distributed systems
Excellent coding skills in Go and Python
Deep understanding of distributed systems fundamentals (consensus concepts, replication, consistency trade-offs)
Deep understanding of networking & performance (RPC patterns, load balancing, latency analysis)
Deep understanding of reliability engineering (timeouts, retries, idempotency, circuit breaking, chaos/failure testing)
Experience scaling services and data flows in cloud environments (AWS/GCP/Azure)
Comfortable working in ambiguity and moving quickly without compromising core quality
Preferred
Experience with high-scale systems: streaming, queues, event-driven architectures, or large-scale caching
Familiarity with Kubernetes and cloud-native infrastructure (helpful, but not the focus)
Experience with ML/AI infrastructure or compute-heavy systems (e.g., GPU scheduling, batch/online hybrid workloads)