Cerebras is developing 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.
As an ML Frameworks Software Engineer on our team, you will work with leaders from industry and academia at the intersection of hardware and software, to develop state-of-the-art solutions for emerging problems in AI compute.
The Cerebras software platform is designed to be targeted by today’s most relevant machine learning frameworks, such as TensorFlow, PyTorch, Caffe2, and MXNet. Our ML software engineers are responsible for integrating these frameworks to work with our own highly optimized software stack. Fundamentally, you will be enabling ML researchers to use the software tools and workflows of today to unlock the advanced hardware capabilities of tomorrow.
About the Role
In this role, you will create tools and design workflows that enable the development, training, and deployment of machine learning models on our new hardware system. Part of this task involves mapping abstract computations expressed via third-party ML frameworks into representations that can then be compiled into highly optimized executables that target Cerebras’s system.
- Develop connections between representations of existing deep learning frameworks — such as TensorFlow, Caffe/2, MXNet, CNTK — with our customized back-end;
- Understand the runtime environments of existing frameworks and our backend, and develop an execution model connecting them together in a way that is seamless to the user
Skills & Qualifications
- Bachelor’s / Master’s degree or foreign equivalent in Computer Science, Engineering, or related field.
- Understanding of state-of-the-art deep learning model architectures and training protocols.
- Direct experience with one ML framework internals (like TensorFlow, PyTorch, ONNX, etc) strongly preferred
- Strong Python and C++ development skills.