About the job
Who We Are
At Ema, we are pioneering cutting-edge AI technologies designed to enhance the creativity and productivity of employees across enterprises. Our unique technology enables organizations to entrust Ema, the AI employee, with repetitive tasks, freeing up human talent for more strategic initiatives. Founded by a team of former executives from tech giants such as Google, Coinbase, and Okta, and backed by prestigious investors including Accel Partners and Naspers, we are on the forefront of the AI revolution.
Our diverse team, composed of top engineers from leading tech firms like Microsoft Research and Facebook, brings a wealth of knowledge and experience, primarily from renowned institutions such as Stanford and MIT. Operating out of Silicon Valley and Bangalore, India, we embrace a hybrid work model, requiring employees to be in our Mountain View, CA office three days a week.
Who You Are
We seek passionate and innovative Machine Learning Engineers who thrive on tackling complex challenges and enjoy working with vast datasets. You have a talent for translating theoretical concepts into practical, scalable solutions, and you are a collaborative team player who excels in autonomous settings. Your enthusiasm for employing machine learning techniques, particularly in Natural Language Processing and Information Retrieval, is matched only by your desire to contribute to a mission-driven, high-growth startup making a significant impact.
Your Responsibilities
Design, develop, and deploy machine learning models that drive our NLP, retrieval, ranking, reasoning, dialog, and code-generation systems.
Implement state-of-the-art machine learning algorithms, including Transformer-based models and reinforcement learning, to enhance AI system performance.
Analyze and process large, complex datasets (structured, semi-structured, and unstructured) to inform model development.
Engage throughout the entire machine learning model lifecycle, from problem identification and data exploration to feature engineering and model evaluation.

