Staff Software Engineer - Inference & Performance
New
R
RunwareAI Infrastructure
United KingdomFull-TimeStaff
Salary not disclosed
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Job Details
- Required Skills
- PHPPythonGoRustDistributed Systems
Requirements
- Software engineering expertise with a focus on backend and systems (PHP, Python, Go, Rust, or similar)
- Proven experience building and operating high-performance, low-latency distributed systems in production
- Deep understanding of asynchronous processing, queues, concurrency models, and back pressure
- Strong intuition for performance trade-offs across CPU, GPU, networking, storage, and application layers
- Experience making and defending critical architectural decisions in complex systems
- Hands-on experience troubleshooting real production issues under load (latency, saturation, cascading failures)
- Familiarity with modern cloud infrastructure, CI/CD, and observability stacks (metrics, tracing, profiling)
- Ability to communicate clearly and influence across teams in a remote-first environment
- Strong mentorship mindset
Responsibilities
- Own end-to-end inference performance across the platform, with clear responsibility for latency, throughput, and reliability targets
- Lead the architecture and design of core inference systems, including request routing, async execution, queuing, GPU scheduling, and result delivery
- Drive the platform toward sub-1 second inference where feasible, identifying bottlenecks across networking, services, storage, and GPU execution
- Make high-impact architectural decisions with performance, scalability, and operational simplicity as first-class concerns
- Partner with ML and model teams to ensure models are production-ready from a performance perspective
- Define performance budgets, SLAs, and success metrics
- Lead deep-dive investigations into latency spikes and system-level performance issues
- Influence and mentor engineers across teams on performance engineering and distributed systems
- Improve tooling, observability, and profiling capabilities
- Advocate for pragmatic engineering best practices around testing, benchmarking, and documentation
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