Staff Software Engineer, Inference Platform

Cerebras Systems•Sunnyvale, CA

10h•Onsite

About The Position

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. Our novel wafer-scale architecture provides the AI compute power of dozens of GPUs on a single chip, with the programming simplicity of a single device. This approach allows Cerebras to deliver industry-leading training and inference speeds and empowers machine learning users to effortlessly run large-scale ML applications, without the hassle of managing hundreds of GPUs or TPUs. Cerebras' current customers include top model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference. Thanks to the groundbreaking wafer-scale architecture, Cerebras Inference offers the fastest Generative AI inference solution in the world, over 10 times faster than GPU-based hyperscale cloud inference services. This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation. We're hiring a Staff Engineer to help lead, drive, and contribute to projects on our Inference Platform team. Our team primarily owns the orchestration layer that runs inference on our datacenter clusters which glues together the cloud components to the ML components. We are often the first team to face issues that haven’t been solved yet so we get to lead the company on a wide variety of solutions, from k8s operators to security policies of services and CI/CD. This is a hands-on TL role for an engineer who will split their time between design, mentoring, and coding and should be experienced in all facets of development including; testing, continuous development, observability, security, networking, debugging, productionization. If you're interested in building our next-generation architecture of a globally distributed inference platform, we'd like to talk.

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. Experience with TTFT and tail-latency reduction is a strong plus.
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.

Nice To Haves

Experience with ML inference infrastructure, model serving systems, or GPU-accelerated workloads is a plus.

Responsibilities

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