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**Senior Machine Learning Engineer

2 months ago

Brampton, Ontario, Canada Charger Logistics Full time
About Us

Charger Logistics Inc. is a leading asset-based carrier with a strong presence across North America. With over two decades of experience in providing top-notch logistics solutions, we have evolved into a world-class transport provider and continue to expand our operations.

We invest heavily in our employees, providing them with the resources and support needed to learn and grow their expertise. Our organization is entrepreneurial-minded, welcoming and supporting individual ideas and strategies. We are currently seeking a motivated individual to join our team based out of our Brampton office.

Job Summary

We are looking for a highly skilled Senior Machine Learning Engineer to join our team. The successful candidate will be responsible for designing and developing conversational systems using language models to guide and understand customer queries. The ideal candidate will have a strong background in machine learning, experience with large-scale distributed data processing systems, and expertise in deploying highly scalable, business-critical ML models in production.

Key Responsibilities
  1. Conversational System Development: Design and develop conversational systems using language models to guide and understand customer queries.
  2. Ranking Model Development: Learn from customer data and build ranking models to create a personalized search experience.
  3. Machine Learning Lifecycle Management: Contribute to all processes of the ML lifecycle: data collection, annotation, modeling, evaluation, deployment, and monitoring.
  4. Vector Embedding Search: Use vector embedding search and information retrieval techniques to find relevant data.
  5. System Scaling: Scale the systems to handle 1 billion searches per day and be highly performant.
  6. Cost vs Performance Optimization: Manage cost vs performance tradeoffs and make the systems operationally efficient.
  7. Collaboration: Collaborate with applied scientists and engineers to architect using the right models and tools.
  8. Knowledge Sharing: Share your knowledge and effectively communicate your ideas to both technical and non-technical audiences.
  9. Science Project Coordination: Be the focal point for science projects, coordinating deliverables, writing papers, modeling, authoring designs, and creating science plans.
  10. LLM Advancements: Keep up with the latest advancements in the LLM, Conversational system space, iterate, experiment, and bring the right toolkit.
Requirements
  1. Machine Learning Experience: 5+ years of hands-on experience building ML/AI systems for search advanced information retrieval, ranking, or recommender system.
  2. Machine Learning Tools: Expertise in machine learning tools and packages like Scikit-learn, SciPy, Tensorflow, Keras, etc.
  3. Distributed Data Processing: Expertise with large-scale distributed data processing systems such as Hive, Hadoop, Spark, etc.
  4. SQL Skills: Strong SQL skills and experience with data manipulation tools like Pandas, Numpy, etc.
  5. Transformer Architectures: Familiarity with different Transformer-based architectures and their applications.
  6. Production ML Models: Expertise in deploying highly scalable, business-critical ML models in production.
  7. Kubernetes-based Tools: Knowledge of Kubernetes-based tools for machine learning like Kubeflow and KServe.
  8. Monitoring and Logging: Experience with monitoring and logging tools that integrate with Kubernetes.
  9. MLOps Best Practices: Familiar with MLOps best practices and production ML lifecycle management.
  10. Language Model Fine-tuning: Fluent in building fine-tuned language models for solving domain-specific problems.
  11. Responsible AI Development: Prior experience with responsible AI development, tuning models for ethical / fairness considerations.