Top Tech Jobs & Startup Jobs in Toronto

The Applied Machine Learning Research Scientist will translate ML techniques into scalable systems, improve LLM performance, and optimize workflows while collaborating closely with researchers and engineers.

Join the Kernel Reliability team to improve the reliability of compute clusters, assist in debugging, and enhance system tools alongside hardware teams.

The role involves owning and scaling critical components of the Cerebras Developer Console, managing both frontend and backend systems, making architectural decisions, and leading technical execution in a fast-paced environment.

Design and implement low-level components in a compiler toolchain, focusing on LLVM for optimizing code generation for a large AI chip architecture.

The Deployment Engineer will build and operate AI inference clusters, ensure scalable deployments, optimize allocation, and maintain infrastructure. Responsibilities include software updates, telemetry development, and collaborative improvements with teams.

The Manufacturing Bring-up Engineer oversees system testing and validation, collaborating with cross-functional teams to automate workflows and enhance manufacturing efficiencies.

Support system-level bring-up processes in manufacturing, collaborate on automation and validate system performance, resolve technical issues and track metrics for continuous improvement.

Responsible for software integration and quality for Cerebras AI platform, focusing on automation, debugging, and cross-team collaboration. Develops QA strategies and testing methodologies to ensure product quality.

Develop and automate configuration for distributed clusters, create monitoring tools, manage cloud operations, and ensure system reliability for Cerebras AI supercomputers.

Design and develop high-performance distributed software for scalable AI training systems, focusing on data pipelines and system efficiency.