Senior Software Engineer

We’re working with 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 the core systems underpinning a next-generation AI product.

This is a hands-on role for someone who enjoys bleeding-edge tech and thrives on complex, unsolved engineering problems - the kind where you’ll be building new primitives, not just wiring together existing ones.

You’ll work on the heart of the platform:
low-latency services, high-throughput pipelines, scalable data and compute orchestration
, and the reliability foundations required to run production-grade systems at pace.

They’re specifically looking for a strong coder with production experience in Python and Go
.

• 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, back pressure
  , 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.

• Excellent coding skills in Go and Python
  .
• Deep understanding of:
• Distributed systems fundamentals (consensus concepts, replication, consistency trade-offs)
• Networking & performance (RPC patterns, load balancing, latency analysis)
• 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.

• 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).