Full Stack LLM Engineer
Cerebras · Toronto, CAN
CHECKED 7H AGO
Infrastructureremote
At a glance
Highlights
- Rapidly bring up SOTA open-source and proprietary models on CSX systems.
- Drive unprecedented performance, efficiency and scalability for AI applications.
- Help deliver 10x faster inference than GPU-based cloud services.
What they 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.
Ideal candidate
A versatile system-minded generalist who thrives in fast-paced bring-up environments and loves debugging across the full stack to unlock breakthrough AI performance.
Requirements
- 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.
Lab profile
About Cerebras
Builds wafer-scale AI chips and cloud systems for ultra-fast training and inference.
What they build
- Wafer-scale AI chips
- AI training systems
- Cloud inference service
Known for
- 56x larger chips than GPUs
- 10x faster inference than GPU clouds
- Industry-leading training speed