About the job
Become a part of Two Dots as we strive to create a more robust financial ecosystem.
In our fast-paced world, every time an individual seeks a mortgage, car loan, or apartment lease, they present financial documents that contribute to their financial profile. The accuracy of these profiles plays a crucial role in stabilizing the economy.
At Two Dots, we are innovating a system that evaluates consumers in a consistent and fair manner. Our mission is to detect fraud that often goes unnoticed and to identify value in unconventional applications that might be overlooked.
Please note that all full-time employees are required to work from our headquarters located in San Francisco, CA.
Role Overview:
We are seeking our second Machine Learning Engineer to collaborate closely with our CTO and Staff ML Engineer. In this position, you will be responsible for designing, developing, and deploying machine learning solutions, particularly focusing on fine-tuning multimodal large language models (LLMs) to address real-world challenges. The right candidate will possess a fervor for building and implementing advanced ML applications, aiming to enhance our automation rates for application approvals/denials and elevate our fraud detection capabilities, ultimately driving business impact and client satisfaction.
Key Responsibilities:
Independently design, develop, and deploy machine learning models.
Examine extensive datasets to reveal insights and patterns that guide product development and enhance personalized customer experiences.
Continuously assess and refine the performance of deployed models to ensure they fulfill business objectives and scalability needs.
Keep abreast of the latest developments in machine learning, AI, data science, and engineering, applying this knowledge to enhance our products and services.
Desirable Traits:
3+ years of experience in a Machine Learning or Data Engineering role, with a strong command of Python and ML frameworks like PyTorch.
Demonstrated ability to enhance models for key information extraction, including named entity recognition and financial document classification.
Experience with active learning and HITL-driven workflows; collaborating with large labeling and quality teams is advantageous.
Exceptional problem-solving skills, with the ability to think critically and creatively.
