Source Job

• Use financial analysis, modeling, and advisory experience to evaluate AI content.
• Provide feedback to help AI understand financial concepts.
• Work independently on a flexible schedule with no minimum hour requirement.

Handshake is connecting students and employers. Through Handshake, finance professionals help AI to better understand financial concepts, quantitative reasoning, industry terminology, and professional communication.

• You will help build AI training data.
• Data will capture how private equity and growth equity professionals evaluate investments.
• Data will span the full investment lifecycle from sourcing to exit.

Mentis AI, founded by ex-Lazard and Partners Group professionals, brings real-world expertise from finance into AI model training. They are headquartered in London and San Francisco and aim to bridge the gap between human and artificial intelligence.

• Construct expert benchmarks to evaluate AI systems.
• Stress-test model reasoning, identifying failures in logic.
• Design frameworks translating investor strategies into AI.

Mentis AI operates at the intersection of private markets expertise and frontier AI systems. Their team combines institutional finance experience with machine learning and applied AI research, collaborating with leading AI labs to improve AI reasoning in financial contexts.

• Construct expert benchmarks: Build and validate real-world investment cases and portfolio management frameworks to evaluate AI systems.
• Stress-test model reasoning: Diagnose weaknesses in AI-generated investment analyses, identifying where logic or market intuition fails.
• Design frameworks: Translate how institutional investors evaluate securities and manage risk into problems that push the limits of AI reasoning.

Mentis AI operates where institutional investment expertise meets frontier AI systems. They combine asset management experience with machine learning and applied AI research, collaborating with leading AI labs to improve how models reason and make decisions in financial contexts.

• Focuses on simplifying the infrastructure behind large language model (LLM) integrations, runtime orchestration, and data workflows.
• Work at the intersection of LLM tooling, serverless infrastructure, and financial data systems.
• Make spawning new research pipelines seamless and scalable.

The client is one of the world's fastest-growing AI companies accelerating the advancement and deployment of powerful AI systems. They help customers by working with the world’s leading AI labs to advance frontier model capabilities and leveraging that work to build real-world AI systems that solve mission-critical priorities for companies.

• Engage the model with investment scenarios, analytical questions, and market-based reasoning tasks; verify factual correctness and financial logic.
• Assess the validity of investment reasoning; capture reproducible error traces; and provide structured feedback to improve prompts, evaluation frameworks, and analytical depth.
• Identify where models oversimplify market behavior or misinterpret financial data.

They are evolving large-scale language models from simple conversational tools into systems capable of analyzing financial markets, interpreting investment strategies, and supporting decision-making across asset classes. They seem to have a growing team.

• Build and scale driver-based 3-statement models (P&L, Balance Sheet, Cash Flow) and multi-year projections.
• Perform company and asset valuations using DCF frameworks, comparable company analysis, and precedent transactions.
• Design "what-if" frameworks and sensitivity analysis to quantify risk and upside for stakeholders.

Gratia helps turn talent's potential into fulfilling global careers through professional development and tech-enabled platforms. They are a remote-first, apprenticeship-based platform where opportunity is defined by what you can do and deliver; their clients include global names like USA Today Co., Endeavor, and New York Life.