Machine Learning Runtime Engineer – Internship

Cerebras has developed a radically new chip and system to dramatically accelerate deep learning applications. Our system runs training and inference workloads orders of magnitude faster than contemporary machines, fundamentally changing the way ML researchers work and pursue AI innovation.

We are innovating at every level of the stack – from chip, to microcode, to power delivery and cooling, to new algorithms and network architectures at the cutting edge of ML research. Our fully-integrated system delivers unprecedented performance because it is built from the ground up for deep learning workloads.

Cerebras is building a team of exceptional people to work together on big problems. Join us!

About The Role
As a Runtime Engineer, you will directly impact the performance at which deep learning models are trained on our “distributed systems” hardware and be responsible for enabling next-generation AI applications that require substantial computational capabilities. In this position, you will develop algorithms for execution, acceleration, partitioning, and routing of communication for dataflow graphs on a massively parallel, multi-core architecture.
 
Specific responsibilities may include:

• Be able to understand the flow of data in a distributed system and how to characterize performance pain points
• Develop algorithms for allocation of compute, communication, and memory resources
• Measure, analyze, and improve execution of Runtime software (that is responsible for training large models with massive datasets)
• Integrate successful optimizations into production software stack
• Implement mathematical models in C++ or Python using discrete optimization techniques and standard libraries and packages

Requirements

• Currently enrolled in a University in Computer Science, Computer Engineering, or any other related discipline
• Strong proficiency in C/C++
• Familiarity with Python or other scripting language
• The ability to operate at multiple levels of abstraction in the software stack

Preferred

• Knowledge about distributed systems, memory subsystem of modern computers, and networking solutions

Term Length

• 12-16 months starting May 2025

Please apply to the job with BOTH your resume and transcript (official or unofficial).