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.
Location: Sunnyvale
We're hiring a Principal Engineer for our Inference Cloud Platform. This team owns the cloud layer behind our Inference Service, including availability, latency, reliability, and multi-region scale.
This is one of the most senior IC roles on the team, for someone who can identify the highest-leverage platform problems, set direction across multiple teams, define long-term architecture, and write production code on critical paths.
Many of the key decisions are ambiguous at the outset; you’ll need to frame the problem, make tradeoffs, and drive execution without a clear spec.
The scope includes multi-region traffic architecture, graceful degradation under bursty AI workloads, high-QPS performance, and the operating model for a platform that needs to remain fast and available under changing demand. You'll partner closely with ML, Product and Infrastructure teams.
Responsibilities
- Problem Definition & Prioritization. Identify the most important technical problems for the platform, often before there's a clear ask. Make explicit tradeoff decisions about what the platform will and won't support, with reasoning that holds up under scrutiny from senior engineering leadership.
- Platform Direction. Set the long-term technical direction for the Inference Cloud Platform, including multi-region topology, failure domains, service boundaries, and system evolution over time.
- Reliability & Performance. Architect active-active systems with rapid failover and graceful degradation (circuit breaking, backpressure, load shedding) with clear SLOs. Drive improvements in latency, throughput, capacity efficiency, and resilience under unpredictable demand.
- Code & Design Reviews. Contribute production code in critical paths, review designs and implementations, and make architectural decisions including build-vs-buy tradeoffs with long-term operational consequences.
- 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 Strategy Beyond Your Team. Drive platform-wide decisions across adjacent teams on reliability, API design, capacity planning, and deployment strategy through strong technical judgment. Translate product and business requirements into scalable system designs and drive alignment on shared infrastructure decisions.
- Mentorship. Raise the quality of technical decision-making across teams through design feedback, pairing, and clear engineering standards.
Skills & Qualifications
- 10+ 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 in cloud environments, including networking, compute orchestration, container platforms, and multi-region production services.
- Strong track record of making sound architectural decisions for highly available, latency-sensitive systems at scale, demonstrated through systems you built directly.
- 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++, or Python, with the expectation that you can contribute production code directly.
- Experience designing observability and reliability practices, including metrics, logging, tracing, alerting, incident response, and SLI/SLO/SLA-driven operations.
- Ability to influence senior engineers, technical leads, and cross-functional partners through technical credibility, communication, and judgment.
- Experience with ML inference infrastructure, model serving systems, or GPU-accelerated workloads is a plus.
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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Skills Required
- 6+ years in high-scale software engineering
- 3+ years leading distributed systems or ML infra teams
- Proven track record scaling LLM inference
- Expertise in distributed inference/training for modern LLMs
- Hands-on experience with model-serving frameworks (e.g. vLLM, TensorRT-LLM, Triton)
- Deep experience with orchestration (Kubernetes/EKS, Slurm)
- Background in monitoring and reliability engineering (Prometheus/Grafana)
Cerebras Systems Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Cerebras Systems and has not been reviewed or approved by Cerebras Systems.
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Fair & Transparent Compensation — Pay is considered competitive for an AI‑hardware firm, and many employees are described as generally happy with compensation. Sentiment indicates compensation is viewed favorably while acknowledging variation by role and seniority.
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Healthcare Strength — Health coverage is described as top quality with medical, dental, and vision included. Premiums are reportedly fully covered for employees in some plans, increasing perceived value.
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Flexible Benefits — Work‑from‑home flexibility is regarded as strong. Flexible arrangements complement standard offerings like vacation, sick leave, and paid holidays.
Cerebras Systems Insights
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/
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