Top Tech Jobs & Startup Jobs in Toronto

As a Senior ML Quality Engineer, you will ensure the quality of software across various ML workloads and workflows by developing testing practices, validating model training accuracy, and debugging components. You will automate workflows and contribute significantly to product quality within a fast-paced environment.

The Senior ML Integration and Ops Engineer will drive technical projects for large-scale LLM model training, focusing on software and hardware integration, automation of workflows, and software specification development. Responsibilities include debugging, effective communication, and enhancing ML product quality, while leading cross-functional efforts and improving integration methodologies.

In this role, the Senior ML Frameworks Engineer will lead a team in integrating machine learning frameworks with Cerebras' advanced software and hardware ecosystem, design APIs for ML models, and optimize systems for high throughput and low latency. The focus will be on collaboration, software quality, and advancing ML solutions.

As a Platform Software Engineer at Cerebras, you'll optimize their AI cloud platform for model training and inference, focusing on latency, load balancing, and system reliability. Responsibilities include defining production requirements, implementing scaling strategies, managing API servers, and resolving system bottlenecks.

The Performance Engineer will build performance models for deep learning applications, optimize kernel microcode and compiler algorithms, debug runtime performance, and design features for ML architectures. Responsibilities also include creating tools for visualizing performance data from the Cerebras WSE and compute clusters.

The ML Stack Optimization Engineer will design, develop, and optimize compiler technologies for AI chips using LLVM and MLIR. They will address performance bottlenecks, work with machine learning teams to integrate optimizations, and contribute to advancing compiler technologies for enhanced performance in AI applications.

The ML Software Tool Development Engineer is responsible for designing and expanding build tools for new hardware and software, resolving issues for ML applications, and collaborating with cross-functional teams to enhance solutions. Candidates should have skills in hardware debugging, CAD tool design, and UI/UX data visualization, with a strong knowledge of C++ and Python.