Environmental Management Authority is hiring a
Machine Learning Engineer

Summary

The job is for a Machine Learning Engineer at Ema, a high-growth startup in Silicon Valley. The role involves developing machine learning models, working with large datasets, and communicating technical workings to stakeholders. Remote positions are available in Canadian cities.

Requirements

• A Master’s degree or Ph.D. in Computer Science, Machine Learning, or a related quantitative field
• Proven industry experience in building and deploying production-level machine learning models
• Deep understanding and practical experience with NLP techniques and frameworks, including training and inference of large language models
• Deep understanding of any of retrieval, ranking, reinforcement learning, and agent-based systems and experience in how to build them for large systems
• Proficiency in Python and experience with ML libraries such as TensorFlow or PyTorch
• Excellent skills in data processing (SQL, ETL, data warehousing) and experience working with large-scale data systems
• Experience with machine learning model lifecycle management tools, and an understanding of MLOps principles and best practices
• Familiarity with cloud platforms like GCP or Azure

Responsibilities

• Conceptualize, develop, and deploy machine learning models that underpin our NLP, retrieval, ranking, reasoning, dialog and code-generation systems
• Implement advanced machine learning algorithms, such as Transformer-based models, reinforcement learning, ensemble learning, and agent-based systems to continually improve the performance of our AI systems
• Lead the processing and analysis of large, complex datasets (structured, semi-structured, and unstructured), and use your findings to inform the development of our models
• Work across the complete lifecycle of ML model development, including problem definition, data exploration, feature engineering, model training, validation, and deployment
• Implement A/B testing and other statistical methods to validate the effectiveness of models. Ensure the integrity and robustness of ML solutions by developing automated testing and validation processes
• Clearly communicate the technical workings and benefits of ML models to both technical and non-technical stakeholders, facilitating understanding and adoption