Asset Management Professionals - AI Residency (Part-time) | Frontier AI Research

Mentis Technology Solutions Inc.

Job Overview

Location

United Kingdom

Job Type

Contract

Full Job Description

📋 Description

• This part-time AI Residency offers senior investment professionals a unique opportunity to bridge institutional asset management expertise with cutting-edge AI research, directly contributing to how frontier models reason about financial markets and portfolio decisions.
• Your work will help shape the evaluation and improvement of AI systems in high-stakes investment contexts, ensuring they align with professional judgment and institutional best practices.
• Day to day, you will develop and refine realistic investment prompts that test AI-generated insights, simulating the queries a junior analyst might pose to evaluate model relevance and accuracy.
• You will systematically compare human investment reasoning with AI outputs across equities, fixed income, and multi-asset strategies, identifying gaps in logic, assumptions, and risk assessment.
• You will design structured frameworks that translate institutional investment processes—such as security valuation, portfolio construction, and risk management—into evaluable challenges for AI systems.
• You will construct and validate expert benchmarks using real-world investment cases, securities analyses, and portfolio management frameworks to rigorously test frontier AI performance.
• You will gain hands-on exposure to how AI is transforming investment workflows, from research and portfolio construction to client reporting and risk attribution, positioning you at the forefront of financial innovation.
• You will collaborate with a multidisciplinary team of AI researchers and investment professionals from Mentis AI, drawing on deep institutional experience from firms like Lazard and Partners Group, and engaging with leading AI labs.
• Through this residency, you will deepen your understanding of AI’s capabilities and limitations in finance, enhancing your ability to critically assess and leverage AI tools in future investment roles.
• You will contribute to meaningful research that advances the reliability and applicability of AI in institutional finance, with potential influence on industry-wide practices and model development.

🎯 Requirements

• 4+ years of experience at a major long-only institutional asset manager (e.g., BlackRock, Vanguard, Fidelity, Capital Group, T. Rowe Price, Wellington, PIMCO, MFS, Neuberger Berman, Jennison, Artisan Partners)
• Senior Research Analyst or Portfolio Manager level with deep expertise in equities, fixed income, or multi-asset portfolio management
• Proven experience conducting fundamental analysis, writing investment commentary, and engaging with company management or issuers
• Familiarity with client reporting, risk attribution, and investment policy constraints
• Strong intellectual curiosity about the application of AI in institutional finance
• Ability to commit 15+ hours per week with flexible scheduling

🏖️ Benefits

• Hourly compensation based on experience, reflecting the value of your institutional expertise
• Fully remote work arrangement with global flexibility, allowing participation from anywhere
• Immediate start date with rolling admissions, enabling timely onboarding
• Direct collaboration with frontier AI researchers and institutional investment experts
• Early, practical exposure to how advanced AI models reason about financial markets and investment decisions
• Opportunity to shape the future of AI in asset management through expert benchmarking and framework design

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Mentis Technology Solutions Inc.

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About Mentis Technology Solutions Inc.

Mentis provides enterprise data-centric security solutions focused on dynamic data masking, sensitive data discovery, database activity monitoring, and compliance reporting. Founded in 2004, the company serves Fortune 500 organizations and government agencies worldwide, helping protect structured and unstructured data across relational databases, big data platforms, and mainframe environments through continuous classification, real-time masking, and risk analytics without altering source data or impacting performance.

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