AI/ML Distributed Systems Engineer
Interop Labs | Toronto | apply.workable.com |
We are actively seeking a talented individual to join our team as an AI distributed systems engineer with a keen interest in blockchain technology. This position is integral to a novel project that synergizes AI and blockchain expertise. The individual will play a pivotal role in crafting large-scale AI systems for training and inference within the dynamic intersection of blockchain and AI.
Requirements
• BSc, MSc or Ph.D. specializing in distributed systems and AI.
• Excellence in Python, Rust, and/or Go programming languages.
• Expertise in building and leveraging large-scale AI systems for training and inference such as Hugging Face, vLLM, CUDA.
• In-depth understanding of performance and system bottlenecks in AI applications.
• Knowledge of the performance characteristics of AI workloads and optimization strategies.
What you will do
• Design and build efficient compute environments for AI/ML workloads on the intersection of distributed systems/blockchain technologies.
• Build and extend AI compute engines to achieve cutting edge performance for specific workload footprints.
• Evaluate models and workloads on different hardware and software stacks.
• Collaborate with cross-functional teams to design, implement, and optimize the AI engines.
Requirements
• BSc, MSc or Ph.D. specializing in distributed systems and AI.
• Excellence in Python, Rust, and/or Go programming languages.
• Expertise in building and leveraging large-scale AI systems for training and inference such as Hugging Face, vLLM, CUDA.
• In-depth understanding of performance and system bottlenecks in AI applications.
• Knowledge of the performance characteristics of AI workloads and optimization strategies.
What you will do
• Design and build efficient compute environments for AI/ML workloads on the intersection of distributed systems/blockchain technologies.
• Build and extend AI compute engines to achieve cutting edge performance for specific workload footprints.
• Evaluate models and workloads on different hardware and software stacks.
• Collaborate with cross-functional teams to design, implement, and optimize the AI engines.
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