About Us
Our mission at Eilla AI is to redefine one of the world's most complex workflows - the one of investors and investment bankers. We're developing a platform for mergers and acquisitions (M&A), venture capital (VC) and private equity (PE) professionals powered by large language models (LLMs) to seamlessly deliver accurate research and analysis. Recently fundraised and already working with leading financial institutions, we want to unlock efficiencies across every stage of the deal workflow in private markets. Join a world-class team with extensive AI and Finance experience on this exciting journey to reshape the future of the industry.
About You
If you:
- want to be an integral part of a fast-paced AI startup
- are self-motivated with a willingness to take ownership of tasks
- have a passion for shipping quality products
- are a team player – willing to do a variety of tasks that move the team forward
- like tackling complex problems and adapting to unexpected challenges
- are a culture builder with a strong sense of integrity
- are excited about developing cutting-edge ML solutions
… and thrive on pushing boundaries and achieving ambitious goals, we’d love to hear from you.
Qualifications
- 3+ years of relevant Data Science/ ML experience at product-driven companies.
- Solid experience with deep learning libraries such as Transformers, PyTorch, TensorFlow, Jax, or Keras.
- Proficiency in Python, with a strong grasp of Git workflows & commitment to best coding practices.
- Ability to leverage cloud computing environments including distributed clusters and GPUs.
- Demonstrated ability to deploy ML applications using containerization technologies is a bonus.
Responsibilities
- Develop and refine scalable machine learning libraries tailored to the financial sector's needs.
- Write, document, and maintain modular code bases, ensuring quality and sustainability in collaboration with our engineering team.
- Report and present research findings and developments including status and results clearly and efficiently both internally and externally, verbally and in writing.
- Design and implement machine learning models to parse and learn from diverse data sets, including but not limited to textual, tabular, and graph data, optimizing for the financial domain.
- Harness the capabilities of Large Language Models (LLMs) to create industry-specific solutions, from proof of concept to fully-fledged deployed applications.