Sr. Machine Learning Engineer

Charger Logistics Inc. is a world- class asset-based carrier with locations across North America. With over 20 years of experience providing the best logistics solutions, Charger Logistics has transformed into a world-class transport provider and continues to grow. 

Charger Logistics invests time and support into its employees to provide them with the room to learn and grow their expertise and work their way up. We are entrepreneurial-minded organization that welcomes and support individual ideas and strategies. We are currently expanding and looking to add a motivated individual to our team based out of our Brampton office.  Responsibilities 

• Create an engaging experience using language models to guide and understand customer queries. 
• Learn from customer data and build ranking models to create a personalized search experience. 
• Contribute to all processes of the ML lifecycle: data collection, annotation, modeling, evaluation, deployment, and monitoring. 
• Use vector embedding search and information retrieval techniques to find relevant data. 
• Scale the systems to handle 1 billion searches per day and be highly performant. 
• Manage cost vs performance tradeoffs and make the systems operationally efficient. 
• Collaborate with applied scientists and engineers to architect using the right models and tools. 
• Share your knowledge and effectively communicate your ideas to both technical and non-technical audiences. 
• Be the focal point for science projects, coordinating deliverables, writing papers, modeling, authoring designs, and creating science plans. 
• Keep up with the latest advancements in the LLM, Conversational system space, iterate, experiment, and bring the right toolkit. 

Requirements

• 5+ years of hands-on experience building ML/AI systems for search advanced information retrieval, ranking, or recommender system. 
• Expertise in machine learning tools and packages like Scikit-learn, SciPy, Tensorflow, Keras, etc. 
• Expertise with large-scale distributed data processing systems such as Hive, Hadoop, Spark, etc. 
• Strong SQL skills and experience with data manipulation tools like Pandas, Numpy, etc. 
• Familiarity with different Transformer based architectures and their applications. 
• Expertise in deploying highly scalable, business-critical ML models in production. 

• Knowledge of Kubernetes-based tools for machine learning like Kubeflow and KServe. 
• Experience with monitoring and logging tools that integrate with Kubernetes. 
• Familiar with MLOps best practices and production ML lifecycle management. 
• Experience with monitoring and logging tools that integrate with Kubernetes. 
• Fluent in building fine-tuned language models for solving domain-specific problems. 
• Prior experience with responsible AI development, tuning models for ethical / fairness considerations. 

Benefits

• Competitive Salary 
• Healthcare Benefit Package 
• Career Growth