AI Engineer

About Menos AI

Menos AI builds AI-powered research intelligence for institutional asset managers. We help some of the world’s largest investment firms make better decisions by turning unstructured information into structured, actionable, evidence-backed insights.

The Role

We’re looking for AI engineers who do more than write code.

As AI makes building software faster, the hardest problems are increasingly about knowing what to build, how to build it, and whether it actually works.

At Menos, you’ll tackle hard problems where AI meets institutional investing. You’ll shape products, make architectural decisions, and take ideas from early prototypes to production systems used by enterprise clients.

📍 Full-time, hybrid: Greater Chicago Area, Champaign–Urbana, or San Francisco Bay Area

What You’ll Do

• Build AI systems that solve real problems. Design and develop LLM-powered pipelines for information extraction, retrieval, reasoning, and knowledge representation across large, unstructured datasets.
• Engineer for uncertainty. Build evaluation frameworks, analyze failure modes, and systematically improve the accuracy, reliability, and performance of AI applications.
• Make smart architectural decisions. Work with RAG, agentic workflows, knowledge graphs, and multi-model systems. Know when to use an LLM, when a simpler approach is better, and how to balance quality, cost, latency, and scalability.
• Build with AI, not just for AI. Use modern coding agents and AI development tools to speed up implementation, experimentation, and iteration, while keeping strong engineering standards.
• Shape what gets built. Work closely with the team and enterprise clients to understand problems, challenge assumptions, prototype solutions, and set the right product and technical direction.
• Own the outcome. Move between rapid experimentation and production-grade engineering, and take responsibility for your systems from concept through deployment and ongoing improvement.

What You Bring

• Strong software engineering fundamentals, especially in Python, APIs, asynchronous programming, and data systems
• Hands-on experience building and deploying AI/LLM applications, with a clear understanding of their capabilities, limitations, and failure modes
• Experience with NLP, information extraction, embeddings, RAG, or agentic workflows
• Fluency with AI-assisted development tools and coding agents, plus the engineering judgment to guide and evaluate their work
• Strong problem-solving and product instincts: you’re comfortable with ambiguity, making trade-offs, and figuring out what matters rather than just following specs
• A systematic approach to evaluation, experimentation, and continuous improvement
• Clear communication and the ability to work with both technical teams and enterprise stakeholders

Nice to Have

• Experience working directly with customers or owning technical products end to end
• Familiarity with financial markets, investment research, or financial data
• Experience with knowledge graphs, structured knowledge extraction, or LLM evaluation and benchmarking
• An advanced degree in Computer Science, Machine Learning, or a related field

We value engineers who combine strong technical fundamentals, curiosity, and sound judgment, and who are excited to build ambitious products with the tools now available.