Data Platform Engineer

Posted 22 Days Ago
Easy Apply
Sunnyvale, CA
In-Office
160K-250K Annually
Mid level
Artificial Intelligence
The Role
Design, build, and maintain a high-performance cloud data platform on AWS. Scale databases and data lakes, create automation and developer tools, implement monitoring/alerting, and improve platform reliability, security, and cost-efficiency while collaborating cross-functionally.
Summary Generated by Built In

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.

About the Role 

As a Platform Engineer on the Cerebras Data Analytics team, you will design, build, and maintain a reliable, high-performance cloud data platform that enables the company’s success via data-driven insights. Your work will amplify the impact of data engineers and data scientists to address diverse needs across hardware, software, operations, and business intelligence. The ideal candidate is a proactive, independent problem solver who takes pride in delivering efficient and effective solutions. 

Responsibilities 

  • Design, deploy, and maintain core infrastructure in AWS for data storage, processing, and reporting. 
  • Maintain and scale databases and data lakes that store ever-increasing volumes of data. 
  • Create tools, automations, and frameworks to improve developer efficiency. 
  • Create monitoring and alerting systems to effectively identify risks and quickly resolve issues. 
  • Continuously improve the reliability, scalability, and cost-effectiveness of the entire data platform. 
  • Collaborate cross-functionally with IT, Security, and engineering teams to securely integrate data sources and infrastructure in various locations. 

Skills and Qualifications 

Required 

  • 3+ years of professional experience in platform engineering, reliability engineering, DevOps, or an infrastructure-focused role. 
  • 3+ years of hands-on experience in AWS, especially EC2, S3, CloudWatch, and/or IAM. 
  • Proficiency in at least one advanced programming language (e.g. Python, C++). 
  • Experience with relational SQL databases (e.g. PostgreSQL, MySQL). 
  • Experience with Infrastructure-as-Code platforms (e.g. Terraform, AWS CloudFormation). 
  • Experience with Git. 

Preferred 

  • Strong proficiency in Python. 
  • Experience with data engineering, data analytics, and/or business intelligence. 
  • Experience with containerization tools and platforms (e.g. Docker, Kubernetes). 
  • Experience with monitoring/observability tools and platforms (e.g. Datadog, Splunk, Prometheus, Loki, Grafana). 
  • Experience with front-end technologies and frameworks (e.g. HTML, JavaScript). 
  • Experience with networking and cybersecurity. 

The base salary range for this position is $160,000 to $250,000 annually.  Actual compensation may include bonus and equity, and will be determined based on factors such as experience, skills, and qualifications.


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 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.

This website or its third-party tools process personal data. For more details, click here to review our CCPA disclosure notice.

Top Skills

AWS
Aws Cloudformation
C++
Cloudwatch
Ec2
Git
Iam
MySQL
Postgres
Python
S3
Terraform
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The Company
HQ: Sunnyvale, CA
402 Employees
Year Founded: 2016

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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