Full Stack LLM Engineer

Cerebras is hiring a Full Stack LLM Engineer for a remote role open to applicants in Canada. Level rates it ; you can apply on Cerebras.

Level's read on this job

On Level's scale, this LLM engineer role is Builds AI ●●●●: the job is building AI systems.
The posting lists this role as remote.
Tools and skills the posting cites include OpenAI, Llama and PyTorch.
Level lists 121 other open roles at Cerebras.

AI in this role

openaillamapytorchtensorflow

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
We are seeking a versatile and experienced engineer to join our Inference Core Model Bringup team. This team is responsible to rapidly bring up state-of-the-art open-source models (like LLaMA, Qwen, etc) or customer-provided proprietary models on our Cerebras CSX systems. Success in this role requires a system-minded generalist who thrives in fast-paced bringup environments and is comfortable working across the entire Cerebras software stack.
Your work will play a critical role in achieving unprecedented levels of performance, efficiency, and scalability for AI applications.

Responsibilities

Contribute to the end-to-end bring up of ML models on Cerebras CSX systems.
Work across the stack: model architecture translation, graph lowering, compiler optimizations, runtime integration, and performance tuning.
Debug performance and correctness issues spanning model code, compiler IRs, runtime behavior, and hardware utilization.
Propose and prototype improvements across tools, APIs, or automation flows to accelerate future bring ups.

Skills & Qualifications

Bachelor’s, Master’s, or PhD in Computer Science, Engineering, or a related field.
Comfort navigating the full AI toolchain: Python modeling code, compiler IRs, performance profiling, etc.
Strong debugging skills across performance, numerical accuracy, and runtime integration.
Experience with deep learning frameworks (e.g., PyTorch, TensorFlow) and familiarity with model internals (e.g., attention, MoE, diffusion).
Proficiency in C/C++ programming and experience with low-level optimization.
Proven experience in compiler development, particularly with LLVM and/or MLIR.
Strong background in optimization techniques, particularly those involving NP-hard problems.

What We Offer

Competitive salary and benefits package.
Opportunities for professional growth and career advancement.
A dynamic and innovative work environment.
The chance to work on cutting-edge technologies and make a significant impact on the future of AI.

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:

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.

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

Full Stack LLM Engineer at Cerebras rates 95 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.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ 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.

Key facts

Full Stack LLM Engineer is an open job at Cerebras.
Level rates this role AI Level 4 of 4: Builds AI.
The role is based in Toronto, CAN and is marked remote.
The job was posted on July 18, 2025.
Applications are handled by Cerebras. Apply on Cerebras

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

AI ResearchOpenAILlamaPyTorchTensorFlow

Questions you could be asked

Tell me about a research question you investigated. What did you find?
Walk me through how you've used OpenAI in your day-to-day work.
What are the limits of Llama that you've run into, and how did you work around them?
What's a project where you used PyTorch hands-on?
Walk me through how you've used TensorFlow in your day-to-day work.

Adapt your resume

List these exact terms on your resume: AI Research, OpenAI, Llama, 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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