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Cerebras

Staff Kernel Optimzation Engineer

Cerebras is hiring a Staff Kernel Optimzation Engineer for a remote role. Level rates it ; you can apply on Cerebras.

Level's read on this job

  • Level rates this role Builds AI ●●●●: the job is building AI systems.
  • This is listed as a remote role.
  • The posting mentions OpenAI, PyTorch and TensorFlow among its tools and skills.
  • Level classes it as a lead role.
  • Cerebras has 121 other open roles on Level.

AI in this role

openaipytorchtensorflow
ai-research

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; 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.

Cerebras works with the leading 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.

 

About The Role
As a Kernel Engineer on our team, you will develop high-performance software solutions at the intersection of hardware and software, developing high-performance software for cutting-edge AI and HPC workloads. Your focus will be on implementing, optimizing, and scaling deep learning operations to fully leverage our custom, massively parallel processor architecture.
You will be part of a world-class team responsible for the design, performance tuning, and validation of foundational ML and HPC kernels. This includes building a library of parallel and distributed algorithms that maximize compute utilization and push the boundaries of training efficiency for state-of-the-art AI models. Your work will be critical to unlocking the full potential of our hardware and accelerating the pace of AI innovation.
Responsibilities

  • Develop design specifications for new machine learning and linear algebra kernels and mapping to the Cerebras WSE System using various parallel programming algorithms.

  • Develop and debug kernel library of highly optimized low level assembly instruction and C-like domain specific language routines to implement algorithms targeting the Cerebras hardware system.

  • Develop and debug high-performance kernel routines in low-level assembly and a custom C-like (CSL) language, implementing algorithms optimized for the Cerebras hardware system.

  • Using mathematical models and analysis to measure the software performance and inform design decisions.

  • Develop and integrate unit and system testing methodologies to verify correct functionality and performance of kernel libraries.

  • Study emerging trends in Machine Learning applications and help evolve Kernel library architecture to address computational challenges of the start-of-the-art Neural Networks.

  • Interact with chip and system architects to optimize instruction sets, microarchitecture, and IO of next generation systems.

Skills And Qualifications

  • Bachelor’s, Master’s, PhD or foreign equivalents in Computer Science, Computer Engineering, Mathematics, or related fields.

  • Understanding of hardware architecture concepts — must be comfortable learning the details of a new hardware architecture.

  • Skilled in C++ and Python programming languages.

  • Good knowledge of library and/or API development best practices.

  • Strong debugging skills and knowledge of debugging complex software stack.

Preferred Skills And Qualifications

  • Experience in kernel development and/or testing.

  • Familiarity with parallel algorithms and distributed memory systems.

  • Experience in programming accelerators such as GPUs and FPGAs.

  • Familiarity with Machine Learning neural networks and frameworks such as TensorFlow and PyTorch.

  • Familiarity with HPC kernels and their optimization.

  

 

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.

Find out more about what it's like to work at Cerebras here!

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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How we rate this

Staff Kernel Optimzation Engineer at Cerebras rates 99 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

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Skills and AI tools this role asks for

AI ResearchOpenAIPyTorchTensorFlow

Questions you could be asked

  1. Tell me about a research question you investigated. What did you find?
  2. Walk me through how you've used OpenAI in your day-to-day work.
  3. What are the limits of PyTorch that you've run into, and how did you work around them?
  4. What's a project where you used TensorFlow hands-on?
  5. How would you decide a model or AI system is ready to ship?

Adapt your resume

  • List these exact terms on your resume: AI Research, OpenAI, PyTorch, and TensorFlow. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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