Machine Learning Engineer, Ema

• We're looking for 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.
• You are a strong team player but also thrive in autonomous environments where your ideas can make a significant impact.
• You love utilizing machine learning techniques to push the boundaries of what is possible within the realm of Natural Language Processing, Information Retrieval and related spaces.
• Most importantly, you are excited to be part of a mission-oriented high-growth startup that can create a lasting impact
• A Master’s degree or Ph.D. in Computer Science, Machine Learning, or a related quantitative field
• At least 2 years of 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

Desirable

• 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
• Process and analyze 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