Software Engineer, Machine Learning

Ema, Mountain View (Hybrid)

What you will do

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.

Ema, Mountain View (Hybrid)

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.