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Cerebras Systems

Principal Customer Success Engineer

Posted 4 Days Ago
Be an Early Applicant
Remote
30 Locations
Senior level
Remote
30 Locations
Senior level
As a Principal Customer Success Engineer, you'll ensure customer success through technical leadership, assisting with onboarding, integrations, and optimization, while partnering with internal teams to enhance customer experience.
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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 global corporations across multiple industries, national labs, and top-tier healthcare systems. In January, we announced a multi-year, multi-million-dollar partnership with Mayo Clinic, underscoring our commitment to transforming AI applications across various fields. In August, we launched Cerebras Inference, the fastest Generative AI inference solution in the world, over 10 times faster than GPU-based hyperscale cloud inference services.

About The Role 

As a Principal Customer Success Engineer at Cerebras, you will play a critical hands-on role in ensuring the success of our strategic customers. As part of our growing Customer Success and Support team, you will be responsible for enabling our customers to unlock the full value of our systems and services. 

You will lead deep technical engagements across a portfolio of high-value accounts, guiding onboarding, integration, performance optimization, and production scaling. You’ll be the go-to technical partner for customer engineering teams, while collaborating closely with internal stakeholders in Product, Engineering, Sales, and Support. 

This is a senior individual contributor role for an experienced customer-facing technologist who thrives at the intersection of Generative AI models and applications, AI infrastructure, and customer enablement.  

Key Responsibilities 
  • Drive technical success of Cerebras customers; 
    • Serve as the technical lead throughout the customer journey—from onboarding to production scaling—ensuring smooth deployment and long-term value realization.
    • Partner with customers to uncover new opportunities where Cerebras products can drive value, from scaling existing AI workloads to enabling novel applications.
    • Architect integrations and deployment plans aligned with customer goals.
  • Serve as a trusted advisor and technical escalation point for our customers;
    • Provide architectural guidance and best practices for GenAI workflows and applications, model deployment, and performance optimization.
    • Collaborate directly with customer engineering teams to optimize performance, debug complex issues, and remove blockers.
    • Work closely with Support and Engineering teams to manage escalations and drive fast, effective resolution of critical issues.
  • Partner cross-functionally to champion the customer experience; 
    • Share field insights with Product and Engineering to inform roadmap decisions. 
    • Work with Sales and Solution Architects to align on customer success plans and identify growth opportunities.
    • Collaborate with Marketing to highlight successful deployments and customer impact.
  • Mentor and elevate the broader Customer Success and Support team; 
    • Provide technical leadership, documentation, and reusable assets to scale success across the organization.
    • Contribute to internal playbooks and onboarding for Customer Success Engineers and Support Engineers.
Skills And Qualifications 
  • Bachelor’s or Master’s degree in a technical field such as Computer Science, Electrical Engineering, or related discipline.
  • 10+ years of professional experience, with 5–7+ years in customer-facing technical roles (e.g., Customer Success Engineering, Solutions Engineering, Technical Account Management).
  • Strong foundation in LLM inference workloads, AI/ML systems, distributed computing, and infrastructure.
  • Experience deploying and optimizing LLM inference workloads, with a solid understanding of latency, throughput, and token-level. performance metrics, as well as inference toolchains, APIs, and optimization techniques relevant to large-scale model serving is a strong plus.
  • Exceptional communication and collaboration skills; ability to interface with developers, architects, and executive stakeholders.
  • Comfortable leading complex technical discussions and resolving high-severity issues in real time.
  • Passion for customer advocacy and delivering measurable impact through high-touch engagement.
Why Join Cerebras

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:

  1. Build a breakthrough AI platform beyond the constraints of the GPU.
  2. Publish and open source their cutting-edge AI research.
  3. Work on one of the fastest AI supercomputers in the world.
  4. Enjoy job stability with startup vitality.
  5. Our simple, non-corporate work culture that respects individual beliefs.

Read our blog: Five Reasons to Join Cerebras in 2025.

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

Ai/Ml Systems
APIs
Distributed Computing
Genai Workflows
Inference Toolchains

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