Technology Finance - AI trainer

Job Title: Technology Finance - AI trainer

Job Type: Part-time

Location: Remote

Job Summary:

Join our customer's team as a Technology Finance - AI trainer, shaping the future of AI models in real-world finance environments. Collaborate on innovative projects that leverage messy, unstructured data and ambiguous finance artifacts to improve AI performance. Embrace a unique role where your financial expertise and communication skills will make a lasting impact.

Key Responsibilities:

• Train and guide AI models using real-world financial data, spreadsheets, and artifacts.
• Contribute finance domain knowledge to broad and messy data seeding initiatives.
• Support projects with ambiguous or partial data inputs, enabling the AI to learn from "in-the-wild" financial documents.
• Collaborate with finance-adjacent contributors to structure and refine training datasets.
• Seed datasets and workflows with minimal upfront structure, adapting and iterating over time.
• Work closely with AI developers to optimize model reconstruction, normalization, and extension processes.
• Communicate insights and complex financial concepts clearly through both written and verbal channels.

Required Skills and Qualifications:

• Strong background in finance or technology finance, with practical experience handling real-world financial data.
• Exceptional written and verbal communication abilities, including translating finance concepts for technical and non-technical audiences.
• Comfort working with unstructured, messy data and ambiguous inputs in spreadsheet formats.
• Ability to collaborate remotely with global teams across US, Canada, UK, and EU.
• Experience with AI training or a keen interest in advancing machine learning projects within finance contexts.
• Adaptable mindset to seed data and workflows first, allowing structure to emerge through iteration.
• Proactive problem-solving and attention to detail, with the ability to work independently.

Preferred Qualifications:

• Previous involvement in AI model development, data seeding, or finance-related machine learning projects.
• Familiarity with ambiguous data flows and iterative training processes.
• Exposure to cross-functional teams in distributed, remote environments.