Machine Learning Engineer

- 100% Remote within the US / Must have background in financial industry. 

Responsibilities

As a Machine Learning Engineer, you will be a crucial part of our tech team, focused on the innovative application of machine learning techniques across various domains. Your key responsibilities will include:

• Building and enhancing machine learning models through all phases of development, including design, training, validation, and implementation.
• Unlocking insights by analyzing large scales of complex numerical and textual data and identifying trends.
• Collaborating with a cross-functional team, including data engineers, data scientists, and data visualization experts, to deliver impactful projects.
• Researching and evaluating emerging technologies in the field.
• Developing data science solutions leveraging modern tools and cloud computing infrastructure.
• Fulfilling additional duties as assigned.

Qualifications

The ideal candidate will possess:

• A Bachelor’s degree in Computer Science, Mathematics, Physics, Statistics, or a related field.
• Demonstrated experience with model design, training, validation, and monitoring.
• An excellent understanding of machine learning, statistical modeling, and algorithms, including their benefits and drawbacks.
• Advanced proficiency with Python, Jupyter Notebook/Lab, Visual Studio Code, and other languages suitable for large-scale data analysis.
• Experience with cloud computing infrastructure.
• Advanced SQL skills.
• Familiarity with data visualization concepts and tools.
• The ability to translate complex business problems into technical solutions.
• Strong capability to work both independently and as part of a team.
• Exceptional verbal, written, interpersonal, and presentation skills to communicate technical and non-technical information to all levels of management.

Desired Skills

Preferably, candidates will also have:

• An advanced degree in Computer Science, Mathematics, Physics, Statistics, or a related field.
• Experience with Natural Language Processing (NLP).
• Proficiency with deep learning frameworks and infrastructure, such as TensorFlow or PyTorch.
• An eagerness to learn and apply techniques in Large Language Models (LLMs) and Generative AI.
• Expertise in AI model optimization on GPU architecture, including knowledge of C++ and CUDA.
• The willingness to research, develop, implement, and fine-tune LLMs tailored to specific domain knowledge and use cases.
• Knowledge of Machine Learning Ops (MLOps) and CI/CD tools for automating the build, test, and deployment of models in production environments.