About the Role
Opus Resourcing Ltd is seeking a Machine Learning Research Scientist with a blend of ML software product research, new tool development, and strong software engineering skills. This role is crucial for a small, high-profile team focused on systems research and the development of LLM tools to process high volumes of complex text and documents.
This is an entrepreneurial opportunity to join a scientific research team, taking the lead on primary development of tools and contributing to the architecture. The role involves working on various activities, including the research, planning, and development of complex data systems and full-stack systems development to empower complex business areas that process large volumes of text/documentation.
We are looking for an ML Research Scientist who is fascinated by using emerging techniques to solve real business problems and is hands-on in developing cutting-edge data systems tech into production. This new role could suit a recent graduate with relevant commercial experience or a more experienced engineer.
Responsibilities
- Lead the primary development of LLM tools and contribute to system architecture.
- Conduct product research and develop tools for NLM / LLM / Deep Learning Systems.
- Plan and run research projects independently.
- Rank and test early tools/innovations.
- Work on full-stack systems development, potentially including JavaScript / HTML front-end capabilities.
Requirements
- Data-centric solid software development skills in Python and back-end systems.
- Proficiency in Deep Learning Frameworks such as TensorFlow OR Keras OR PyTorch.
- Experience in processing large volumes of text/documents is ideal.
- A self-starter with an entrepreneurial spirit, comfortable working independently and in a small team.
- Proven skills in Python backend systems engineering.
- Experience in CI/CD and software development practices.
- An interest in solving complex business systems with data systems.
- US University degree or master’s in Computer Science (e.g., UCLA, Harvey Mudd, Berkeley, USC, Stanford, MIT, Yale, Carnegie, Caltech, Princeton).
- Applicants must live in California and can be hybrid or predominantly remote, spending approximately one week per quarter in the Los Angeles office.