Staff Software Engineer, Inference Cloud

Responsibilities

• Help shape the technical direction for the Inference Cloud Platform, including multi-region topology, failure domains, service boundaries, and system evolution over time, and own the roadmap for major technical areas.
• Design and build critical platform components such as service discovery, request routing, load balancing, caching, batching, and traffic management for AI inference workloads.
• Architect active-active systems with rapid failover, graceful degradation, and clear SLOs. Drive system-level improvements in latency, throughput, capacity efficiency, and resilience under unpredictable demand.
• Define platform mechanisms for admission control, quota management, rate limiting, and differentiated quality of service across workload types and customer tiers.
• Write and review production code in the most important parts of the platform. Make high-consequence architectural decisions within your area and set the technical bar through design reviews, code reviews, and sound engineering judgment.
• Lead on the hardest production issues and cross-system bottlenecks. Drive observability, incident response, capacity planning, and post-incident improvement with a high standard for operational rigor.
• Partner with ML, Product, Infrastructure, and Platform teams to translate product and business requirements into scalable system designs, and drive alignment on shared technical decisions within your domain and adjacent platform surfaces.
• Raise the effectiveness of senior engineers through design feedback, pairing, and clear technical standards.

Skills & Qualifications

• 8+ years of experience in software engineering, with substantial individual contributor experience building and operating large-scale distributed systems or cloud infrastructure.
• Deep expertise in distributed systems architecture in cloud environments, including networking, compute orchestration, container platforms, and multi-region production services.
• Strong track record of making sound architectural decisions for highly available, latency-sensitive systems at scale.
• Experience optimizing latency, throughput, and efficiency in high-QPS systems. Experience with TTFT and tail-latency reduction is a strong plus.
• Strong proficiency in backend or systems languages such as Go, C++, or Python, with the expectation that you can contribute production code directly.
• Experience designing observability and reliability practices, including metrics, logging, tracing, alerting, incident response, and SLO-driven operations.
• Ability to influence senior engineers and cross-functional partners through technical credibility, communication, and judgment, especially within your domain and adjacent systems.
• Experience with ML inference infrastructure, model serving systems, or GPU-accelerated workloads is a plus.