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Ema is building the next generation AI technology to empower every employee in the enterprise to be their most creative and productive. Our proprietary tech allows enterprises to delegate most repetitive tasks to Ema, the AI employee. We are looking for an innovative and passionate Machine Learning Engineers to join our team. You are someone who loves solving complex problems, enjoys the challenges of working with huge data sets, and has a knack for turning theoretical concepts into practical, scalable solutions.
Job Responsibility:
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
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
Familiarity with the latest industry and academic trends in machine learning and AI, and the ability to apply this knowledge to practical projects
Good understanding of software development principles, data structures, and algorithms
Excellent problem-solving skills, attention to detail, and a strong capacity for logical thinking
The ability to work collaboratively in an extremely fast-paced, startup environment