Responsibilities

Raise the effectiveness of senior engineers through design feedback, pairing, and clear technical standards.
Help shape the technical direction for the Inference Platform, k8s custom resource definitions, failure domains, service boundaries, and system evolution over time; own the roadmap for major technical areas.
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.
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 Cloud 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.

Requirements

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 ideally with kubernetes.
Strong track record of making sound architectural decisions for highly available, latency-sensitive systems at scale.
Experience with security (certificates, TLS, mTLS).
Experience optimizing latency, throughput, and efficiency in high-QPS systems.
Strong proficiency in backend or systems languages such as Go, C++ 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 TTFT and tail-latency reduction.
Experience with ML inference infrastructure, model serving systems, or GPU-accelerated workloads.