About The Role

This teams' principal responsibility is to rapidly bring up state-of-the-art open-source models, frameworks and data engineering. Success in this role requires a system-minded generalist who thrives in fast-paced bringup environments and is comfortable working across the entire 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 frameworks for RL, inference serving, 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 with 10+ years’ experience.
Comfort navigating the full AI toolchain: Python modelling 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.
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.

Lead Full Stack Machine Learning Engineer