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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.
Job Responsibility:
Provide hands-on technical leadership, owning the technical vision and roadmap for the Cerebras Inference Platform, from internal scaling to on-prem customer solutions
Lead the end-to-end development of distributed inference systems, including request routing, autoscaling, and resource orchestration on Cerebras' unique hardware
Drive a culture of operational excellence, guaranteeing platform reliability (>99.9% uptime), performance, and efficiency
Lead, mentor, and grow a high-caliber team of engineers, fostering a culture of technical excellence and rapid execution
Productize the platform into an enterprise-ready, on-prem solution, collaborating closely with product, ops, and customer teams to ensure successful deployments
Requirements:
6+ years in high-scale software engineering
3+ years leading distributed systems or ML infra teams
strong coding and review skills
Proven track record scaling LLM inference: optimizing latency (<100ms P99), throughput, batching, memory/IO efficiency and resources utilization
Expertise in distributed inference/training for modern LLMs
understanding of AI/ML ecosystems, including public clouds (AWS/GCP/Azure)
Hands-on with model-serving frameworks (e.g. vLLM, TensorRT-LLM, Triton or similar) and ML stacks (PyTorch, Hugging Face, SageMaker)
Deep experience with orchestration (Kubernetes/EKS, Slurm), large clusters, and low-latency networking
Strong background in monitoring and reliability engineering (Prometheus/Grafana, incident response, post-mortems)
Demonstrated ability to recruit and retain high-performing teams, mentor engineers, and partner cross-functionally to deliver customer-facing products
Nice to have:
Experience with on-prem/private cloud deployments
Background in edge or streaming inference, multi-region systems, or security/privacy in AI
Customer-facing experience with enterprise deployments
What we offer:
Build a breakthrough AI platform beyond the constraints of the GPU
Publish and open source their cutting-edge AI research
Work on one of the fastest AI supercomputers in the world
Enjoy job stability with startup vitality
Our simple, non-corporate work culture that respects individual beliefs