AI/Machine Learning Research Intern working with AI experts to enhance capabilities of MARCIE platform. Collaborating on complex Content Intelligence challenges in Customer Communication Management.
Responsibilities
Collaborate with the AI team to enhance MARCIE’s features, addressing Content Intelligence needs within Messagepoint’s CCM platform.
Apply Generative AI models and deep learning techniques to: Natural Language Processing (e.g., text generation, entity recognition, sentiment analysis).
Document understanding (e.g., information extraction, classification, summarization).
Image and object detection, semantic segmentation, and visual content analysis.
Context-aware communication generation and optimization.
Conduct research and experimentation to explore innovative AI solutions in CCM-specific scenarios.
Design, implement, and evaluate machine learning models, ensuring scalability and performance.
Leverage distributed computing frameworks such as Spark for handling large datasets efficiently.
Collaborate with cross-functional teams to integrate AI solutions into real-world applications.
Document and present findings, prototypes, and results to stakeholders.
Requirements
Currently enrolled in a degree program (undergraduate or graduate) in Computer Science, Data Science, Artificial Intelligence, or a related field.
Proficient programming skills in Python and/or Java.
Solid understanding of deep learning frameworks such as PyTorch or TensorFlow.
Exposure to Generative AI models (e.g., GPT, Stable Diffusion, or similar architectures).
Familiarity with distributed computing and storage frameworks such as Spark.
Experience or knowledge of applied AI techniques for solving real-world problems.
Strong analytical, problem-solving, and communication skills.
Self-motivated, eager to learn, and able to work collaboratively in a team environment.
Experience with natural language processing libraries such as Hugging Face Transformers, spaCy, or NLTK.
Familiarity with computer vision frameworks like OpenCV or Detectron2.
Knowledge of optimization techniques for scalable AI model deployment.
Understanding of CCM platforms or content intelligence use cases.
Benefits
Hands-on experience working on a cutting-edge AI platform with real-world impact.
Mentorship from industry-leading AI professionals.
A collaborative and innovative work environment.
Opportunities to present your work and gain visibility.
Senior Machine Learning Engineer building causal and decision systems for CSC Generation’s consumer businesses. Developing pricing and other commercial automation using experimentation, uncertainty, and policy learning.
Senior Deep Learning Engineer developing player - tracking, evaluation, and forecasting models for SumerSports’ football intelligence platform. Driving research threads into production for NFL and NCAA teams.
Machine Learning Specialist developing AI, models, and analytical data products for the Government of Alberta. Applying machine learning to improve public services and policymaking.
Machine Learning Engineer developing production AI models and scalable MLOps platforms for Wave, which helps small businesses thrive. Collaborating on financial - risk applications, governance, observability, and reliable deployment.
Senior Geospatial ML Engineer building satellite - imagery vegetation intelligence for a climate - tech company. Improving production models and pipelines to help utilities prevent wildfires and outages.
Senior Machine Learning Engineer building scalable recommender systems for Thomson Reuters’ legal, tax, compliance, government, and media platforms. Implementing secure ML products and infrastructure.
ML Engineer scaling Torc Robotics’ simulation platform for autonomous trucks. Embedding with Autonomy teams to operationalize replay, recompute, metrics, visualization, and model integrations.
Senior ML Engineer building production AI services and MLOps platforms for Hyatt, a global hospitality company. Optimizing cloud - based inference, infrastructure, and model operations.
Machine learning engineer developing real - time underwriting models for Affirm’s buy - now - pay - later platform. Productionizing risk systems, feature pipelines, monitoring, and experimentation workflows.