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 are seeking a talented Platform Software Engineer to join the team building the Cerebras Inference Platform. You will be instrumental in designing, developing, and operating the core backend services and APIs that power the Inference platform. You'll build the software that allows customers to seamlessly deploy, manage, and serve inference workloads on dedicated Cerebras hardware.
- Set Technical Direction: Own the long-term architecture and roadmap for the observability platform across all pillars (metrics, logs, traces, and events). Drive adoption across the engineering organization through RFCs, design reviews, and close partnership with stakeholders.
- Build for Cerebras-Scale: Design telemetry pipelines that handle high-cardinality, high-frequency data from thousands of Wafer-Scale Engines, large Kubernetes clusters, and the storage and networking fabric connecting them. Solve ingestion and query performance problems that break conventional approaches.
- Drive Reliability Across the Organization: Partner with Engineering, SRE, Hardware, and ML Infrastructure teams to define SLOs, build alerting strategies, and create tooling that accelerates root cause analysis and reduces MTTR. Make it easy for any engineer to understand system health and diagnose failures.
- Bridge Hardware and Software Observability: Work with engineering teams accross the stack to surface critical hardware health signals into a unified observability layer alongside software metrics.
- Shape Developer Experience: Design and implement instrumentation libraries and standards (OpenTelemetry) that make rich observability the default for every service, not an afterthought. Establish conventions that scale across teams.
- Mentor and Grow Engineers: Raise the technical bar across the organization. Mentor senior engineers, influence engineering practices across team boundaries, and foster a culture of operational excellence.
Qualifications
- 8+ years of software engineering experience, with 4+ years building or operating observability/monitoring platforms at significant scale (millions of active time series, petabytes of log data).
- Deep expertise in the open-source observability ecosystem. You have operated, tuned, and extended systems like Prometheus, Thanos/Cortex/Mimir, Elasticsearch/ClickHouse, or Loki and understand their internals, failure modes, and cost characteristics.
- Experience with OpenTelemetry for instrumentation across a polyglot services environment.
- Proficiency in Go preferred, with strong experience in Python. You write performant, concurrent systems code, not just glue scripts.
- Strong distributed systems and Kubernetes expertise: sharding, replication, consistency models, monitoring K8s itself and the workloads running on it, and the tradeoffs involved in building reliable data pipelines at scale.
- Experience with observability cost management and capacity planning at scale.
- Track record of setting technical direction and driving adoption across multiple teams. You operate effectively with ambiguity and can translate business needs into technical strategy.
People who are serious about software make their own hardware. At Cerebras we have built a breakthrough architecture that is unlocking new opportunities for the AI industry. With dozens of model releases and rapid growth, we’ve reached an inflection point in our business. Members of our team tell us there are five main reasons they joined Cerebras:
- 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.
Read our blog: Five Reasons to Join Cerebras in 2026.
Apply today and become part of the forefront of groundbreaking advancements in AI!Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer. We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies. We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around them.
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Top Skills
What We Do
Cerebras Systems is a team of pioneering computer architects, computer scientists, deep learning researchers, functional business experts and engineers of all types. We have come together to build a new class of computer to accelerate artificial intelligence work by three orders of magnitude beyond the current state of the art.
The CS-2 is the fastest AI computer in existence. It contains a collection of industry firsts, including the Cerebras Wafer Scale Engine (WSE-2). The WSE-2 is the largest chip ever built. It contains 2.6 trillion transistors and covers more than 46,225 square millimeters of silicon. The largest graphics processor on the market has 54 billion transistors and covers 815 square millimeters. In artificial intelligence work, large chips process information more quickly producing answers in less time. As a result, neural networks that in the past took months to train, can now train in minutes on the Cerebras CS-2 powered by the WSE-2.
Join us: https://cerebras.net/careers/
