CSO/Late Co-Founder
- context
- DeepFlows
- Role Description
- Key Responsabilities & Tech Stack
- Qualification & Required Skills
CSO/Late Co-Founder
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Founded in 2024 by ML & Soft engineers from Google, Polytechnique, Centrale Paris, and a former investment banker, DeepFlows is a fast-growing technology company that automates tedious analytical work for founders, financial advisors and investors. Backed by private investors from J.P. Morgan, Morgan Stanley, Meta, and others, and backed by Microsoft, the platform leverages cutting-edge AI agents to radically accelerate and improve decision-making in M&A, private equity and asset management.
As CSO, you will lead DeepFlows’ scientific and research direction, with a focus on pushing the frontiers of applied AI in financial workflows. You'll drive long-term AI/ML research, define our AI strategy in coordination with product and engineering, and ensure that our agents stay ahead of the curve in terms of accuracy, innovation, and performance.
You’ll collaborate closely with the CEO and CTO, serve as the primary architect for model innovation (RAG, LLMs, knowledge graphs), and represent DeepFlows’ research in academic, industry, and investor circles. Your work will directly inform the models deployed in production and shape how we approach reasoning, search, evaluation, and trust in multi-agent environments.
| Domaine | Missions-clés |
| AI Research Strategy | Define the scientific roadmap for DeepFlows' agents; align model evolution with product goals and market trends |
| Multi-Agent Reasoning | Research and improve orchestration, coordination, and memory across LLM-based agents |
| Retrieval-Augmented Generation | Design and improve RAG pipelines (vector + graph) for financial document analysis |
| Evaluation & Alignment | Build internal benchmarks and observability tools to evaluate hallucination, latency, cost, and decision confidence |
| Model & Prompt Innovation | Own experimentation across model families (GPT-4, Claude, Mistral, etc.); drive prompt engineering and adaptive agent behavior |
| Research & Publication | Contribute to the AI community through whitepapers, open-source work, or conference publications (NeurIPS, ACL, etc.) |
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PhD (or equivalent) in Machine Learning, NLP, Mathematics, or related fields;
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4–6+ years of experience in AI/ML research or applied LLM research;
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Deep understanding of retrieval-augmented generation, LLM evaluation, and scientific modeling;
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Track record of delivering cutting-edge innovation in real-world products or research environments.
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Bonus: experience with financial data, investment workflows, or working with structured + unstructured documents + Publications, patents, or open-source contributions in AI/NLP/IR.