Senior LLM Researcher (Multiple)

Who we are

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 Universal AI employee. We are founded by ex-Google, Coinbase, Okta executives, and serial entrepreneurs. We’re well-funded by the top investors and angels in the world. Ema is based in Silicon Valley and is currently in stealth mode. 

Who you are

We're looking for an innovative and passionate Large Language Model Research Engineer 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 (NLP), specifically building and experimenting with the next generation of Large Language model architectures. Most importantly, you are excited to be part of a mission-oriented high-growth startup that can create a lasting impact.

You will:

1. Conceptualize, develop, and deploy large language models that underpin our retrieval, reasoning, and code-generation systems.
2. Implement advanced large-language model techniques, such as advanced transformer fusion-based architectures, retrieval-based autoencoders, deep prompt learning, and deep ensemble learning to continually improve the performance of our ML systems.
3. Lead the processing and analysis of large, complex datasets (structured, semi-structured, and unstructured), and use your findings to inform the implementation of our models.
4. Work across the complete lifecycle of ML model development, including problem definition, data exploration, feature engineering, model training, validation, and deployment.
5. 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.
6. Clearly communicate the technical workings and benefits of ML models to both technical and non-technical stakeholders, facilitating understanding and adoption.

Ideally, you'd have:

1. A  Ph.D. in Computer Science, Machine Learning, or equivalent industry experience.
2. Proven industry experience in building and deploying production-level machine learning models.
3. Deep understanding and practical experience with Large language model architectures, their advantages, and limitations in specific scenarios. 
4. Deep understanding of deep, reinforcement learning, and agent-based systems and experience in how to build them for large systems.
5. Proficiency in Python and experience with ML libraries such as TensorFlow or PyTorch.
6. Excellent skills in data processing (SQL, ETL, data warehousing) and experience working with large-scale data systems.
7. Experience with machine learning model lifecycle management tools, and an understanding of MLOps principles and best practices.
8. Familiarity with cloud platforms like GCP or Azure.
9. Familiarity with the latest industry and academic trends in machine learning, and the ability to apply this knowledge to practical projects.
10. Good understanding of software development principles, data structures, and algorithms.
11. Excellent problem-solving skills, attention to detail, and a strong capacity for logical thinking.
12. The ability to work collaboratively in an extremely fast-paced, startup environment.