Machine Learning Engineer
Skills
APIData ManipulationData ScienceImage EditingLevel BuildingNatural Language ProcessingNumpy
What the job involves
The main requirements, responsibilities and hiring steps.
Requirements
- Demonstrate solid machine learning engineering experience particularly with NLP applications and unstructured data in a cloud environment.
- Fluent in Python and proficient with Python Data Science Stack including NumPy Pandas and Scikit Learn.
- Familiar with Docker Docker Compose web APIs modular architectures IPC and publish-subscribe systems.
- Able to context switch compartmentalize projects and communicate clearly.
- Comfortable building and training models and using cloud APIs where appropriate.
- Comfortable digesting academic papers but focused on coding practical solutions.
- Proven expertise in building ML applications and customer-facing when needed.
- Able to thrive in rapidly changing environments and pitch creative ideas.
Nice to have
- Familiarity with predicate logic
- Familiarity with Extended Backus-Naur Form (EBNF) notation
- Lisp languages
Day to day
- Develop and implement machine learning models and algorithms to create more reliable AI systems.
- Deliver complex AI-driven solutions for customers focusing on defense applications.
- Implement and build on DSAIL to increase security and reliability for our customers.
- Train AI for human-like reasoning rather than yes/no logic.
- Collaborate with cross-functional teams to integrate ML models into existing systems.
- Conduct data management tasks such as preprocessing feature engineering and model evaluation.
- Address fundamental limitations of LLMs by improving context understanding and logical reasoning.