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Senior Machine Learning Engineer

2 months ago

Brampton, Ontario, Canada Charger Logistics Full time
Unlock Your Potential at Charger Logistics

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

We're an entrepreneurial-minded organization that fosters a culture of innovation and supports individual ideas and strategies. We're currently expanding and seeking a motivated individual to join our team based out of our Brampton office.

Key Responsibilities
  1. Design and implement engaging conversational experiences using language models to guide and understand customer queries.
  2. Analyze customer data to build ranking models that create a personalized search experience.
  3. Contribute to all stages of the machine learning lifecycle, from data collection and annotation to modeling, evaluation, deployment, and monitoring.
  4. Apply vector embedding search and information retrieval techniques to find relevant data.
  5. Scale systems to handle 1 billion searches per day while ensuring high performance.
  6. Optimize systems for operational efficiency, balancing cost and performance.
  7. Collaborate with applied scientists and engineers to architect solutions using the right models and tools.
  8. Effectively communicate technical ideas to both technical and non-technical audiences.
  9. Lead science projects, coordinating deliverables, writing papers, modeling, authoring designs, and creating science plans.
  10. Stay up-to-date with the latest advancements in LLM and conversational systems, iterating and experimenting to bring the right toolkit.
Requirements
  1. 5+ years of hands-on experience building ML/AI systems for search, advanced information retrieval, ranking, or recommender systems.
  2. Expertise in machine learning tools and packages like Scikit-learn, SciPy, Tensorflow, Keras, etc.
  3. Experience with large-scale distributed data processing systems such as Hive, Hadoop, Spark, etc.
  4. Strong SQL skills and experience with data manipulation tools like Pandas, Numpy, etc.
  5. Familiarity with different Transformer-based architectures and their applications.
  6. Expertise in deploying highly scalable, business-critical ML models in production.
  7. Knowledge of Kubernetes-based tools for machine learning like Kubeflow and KServe.
  8. Experience with monitoring and logging tools that integrate with Kubernetes.
  9. Familiar with MLOps best practices and production ML lifecycle management.
  10. Ability to build fine-tuned language models for solving domain-specific problems.
  11. Prior experience with responsible AI development, tuning models for ethical / fairness considerations.