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Machine Learning Engineer - Enterprise

Bosonai
Toronto, CAN
Full-time
Mid Level
Onsite
CAD 150000-400000 yearly
Discovered 3 weeks ago
Free

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  • Deliver solutions end to end that meet the needs of our customers - understanding user pain points, scoping product specs, and designing and building LLM-powered software.
  • Benchmark the model, and help write evals for customers to identify model weaknesses.
  • Develop and deploy modern search systems (e.g., RAG, DeepSearch) to enhance model performance, grounding, and the ability to utilize enterprise-specific knowledge.
  • Implement and optimize techniques for fine-tuning and align large models on domain-specific data.
  • Ensure the quality, reliability, security, and scalability of models and agentic systems through meticulous attention to detail, diligent execution, and continuous monitoring in demanding enterprise settings.
  • Integrate individual AI components into a scalable platform.
  • Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related quantitative field, or equivalent practical experience.
  • Strong contribution record on GitHub. Please include your GitHub link in your application.
  • Experience working with large language or multimodal models and their applications.
  • Experience implementing and working with search systems.
  • Proven ability to pay close attention to detail and prioritize quality, reliability, and security in technical work.
  • Proficiency in programming languages (e.g., Python, Rust, TypeScript or Go) and relevant ML frameworks (e.g., PyTorch, JAX).
  • Demonstrated ability to design, chain, or orchestrate multiple models (especially LLMs) to create multi-step pipelines or workflows for task automation.
  • Experience developing or contributing to agentic AI products or systems.
  • Experience with cloud platforms (AWS, GCP, Azure) and MLOps practices.
  • Familiarity with distributed training and inference techniques.
  • Experience with system design, API development, and building scalable infrastructure for deploying and managing AI models or agentic systems.
  • Understanding of enterprise software integration patterns and data security considerations.
  • Solid understanding of HTTP protocol and real-time communication protocols (e.g., WebRTC) for voice AI.
  • Excellent problem solving skills.
  • Ability to work independently and drive projects forward in a fast-paced environment

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