Software Engineer, Inference Platform

Cerebras Cerebras · Semiconductors · Headquarters +1 · Software

Software Engineer for Cerebras' Inference Platform team, focusing on the orchestration layer for inference on datacenter clusters. Responsibilities include designing, developing, and maintaining production software, shaping platform technical direction, ensuring reliability and performance, and leading on critical production issues. Requires experience with large-scale distributed systems, Kubernetes, and latency-sensitive systems.

What you'd actually do

  1. Design, develop, test, and maintain production software, with responsibilities spanning testing, continuous development, observability, security, networking, debugging, and productionization.
  2. Platform Direction. Help shape the technical direction for the Inference Platform, Kubernetes custom resource definitions, failure domains, service boundaries, and system evolution over time, and own the roadmap for major technical areas.
  3. Reliability & Performance. 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.
  4. Execution on Critical Paths. 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.
  5. Production Leadership. 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.

Skills

Required

  • 3+ years of experience in software engineering
  • building and operating large-scale distributed systems or cloud infrastructure
  • distributed systems
  • Kubernetes
  • building highly available, latency-sensitive systems at scale
  • security (certificates, TLS, mTLS)
  • optimizing latency, throughput, and efficiency in high-QPS systems
  • backend or systems languages such as Go or C++

Nice to have

  • Experience with ML inference infrastructure, model serving systems, or GPU-accelerated workloads
  • TTFT and tail-latency reduction

What the JD emphasized

  • production software
  • Inference Platform
  • Kubernetes
  • latency-sensitive systems
  • high-QPS systems
  • ML inference infrastructure
  • model serving systems

Other signals

  • inference platform
  • datacenter clusters
  • Kubernetes operators
  • service security policies
  • CI/CD
  • globally distributed inference platform
  • latency-sensitive systems
  • high-QPS systems
  • ML inference infrastructure
  • model serving systems