--- title: 'Member of Technical Staff | Quantitative Finance at Poesis' canonical: 'https://feeny.ai/job/member-of-technical-staff-quantitative-finance-poesis-menlo-park-1w1nvw9w85nb' type: 'job' last_seen: '2026-11-04' --- # Member of Technical Staff | Quantitative Finance at Poesis - **Company:** Poesis - **Location:** Menlo Park, CA - **Employment:** full-time - **Work type:** hybrid - **Posted:** 2026-10-13 - **Last confirmed live:** 2026-11-04 - **Apply:** https://jobs.ashbyhq.com/poesis/d5e7c3aa-ae58-4cea-8735-23368cf6e9ec ## Job description ## About Poesis Whoever builds the leading intelligence for finance will create far more than returns. Poesis is the AI-native investment firm running autonomous agents that predict markets, construct portfolios, and manage risk. Our founders managed institutional capital at Capital Group ($3T AUM) and led enterprise ML at Goldman Sachs and Amazon. We're building a new type of firm, where live capital is the training ground for an intelligence that compounds with every signal. ## About the Role We’re looking for an exceptional Member of Technical Staff (Quantitative Finance) to work alongside our CEO, Chief Scientist, and AI and engineering teams on the quantitative systems behind our investment process. You’ll prepare large financial datasets, design and implement models and agents, and build tools for testing and evaluation. This is an opportunity to work close to the research itself, moving new ideas from experiment to production, and seeing your work inform investment decisions. Challenges You'll Work On - Turn financial research and model prototypes into robust production systems. - Make complex financial data reliable enough for rigorous modeling and investment research. - Build scalable, reproducible systems for feature generation, backtesting and model evaluation. - Stress-test models and signals to understand what holds up and what breaks down. - Help solve the engineering challenges of moving research quickly from experiment to production. ## About You - You’ve built infrastructure, data pipelines, or analytical tools for developing and evaluating financial models or trading strategies. - You enjoy working with complex linguistic and numerical data and producing rigorous, reproducible analysis. - You have strong quantitative foundations and can reason clearly about statistical and modeling problems. - You balance iteration speed with engineering quality and know when a prototype needs to be made more robust. - You can explain technical findings and experimental results to leadership and technical and nontechnical audiences. - You have a BS, MS, or PhD in Computer Science, Mathematics, Statistics, Physics, Finance, or a related quantitative field. ## Nice to Have - Full-time experience in finance, data science, or machine learning engineering. - Experience integrating professional financial data from Bloomberg, CapIQ, FactSet, or Refinitiv into quantitative research workflows. - Familiarity with portfolio optimization, risk modeling, or financial time series. Location Hybrid, 3 days per week on-site at our office in Menlo Park, CA. Relocation allowance available. ## Benefits We offer excellent medical, dental, and vision coverage, alongside a strong benefits package that includes catered lunches, commuter benefits, and more. Current legal authorization to work in the US required; continuing work visa sponsorship available for full-time employees. Working at Poesis As an early team member, you’ll help shape not just the product, but how the company operates. Your decisions will have lasting impact across the business. You’ll build from first principles, with no legacy systems, or entrenched processes slowing you down. Our team is made up of people from elite companies and universities who are low ego, collaborative, and excited to build together. ## About Poesis ## Company Overview - **One-liner**: Poesis is an AI-native investment manager building a foundation model for investing, using modular AI systems to predict market movements and outperform legacy managers. - **Entity Type**: Private (Bootstrapped) - **Headquarters**: Palo Alto, California, United States - **Founded**: Not publicly available - **Founders**: Alex Popa (Founder & CEO), Charles Elkan (Chief Scientist) ## Core Business - **Primary industry**: AI-powered asset management / hedge fund management - **Target customers**: Institutional investors, asset managers, and clients seeking AI-driven alpha generation (B2B) - **Mission or purpose**: “Build up a scale manager that never gets tired, predicts change earlier, and delivers consistent outcomes for clients in a world being reshaped by intelligence.” ## Products & Services - **Large Investment Model (LIM)**: A single AI foundation model that powers every product—predicts markets, constructs portfolios, and manages risk. It is continuously trained on live capital markets data. - **AI Agents**: Teams of modular, specialized agents that execute prediction, portfolio construction, and risk management tasks, democratizing access to alpha. ## Market Standing - **Valuation**: $3 million (estimated, 2025 – per GetLatka) - **Key Metric**: Annual Recurring Revenue of $990K (estimated ARR for 2025 – per GetLatka); bootstrapped with no outside funding - **Notable Investors/Partners**: None (bootstrapped) - **Growth Signals**: Headcount grew 100% YoY (7 employees on LinkedIn, ~9 on other sources); active hiring for Head of Engineering, Machine Learning Engineer, and Quantitative Developer; LinkedIn follower growth +4.3% monthly ## Competitive Advantages - First mover in building a **foundation model specifically for investing** (the Large Investment Model) - Team composed of senior talent from **Capital Group, Goldman Sachs, and Amazon**—combining deep capital markets experience with AI expertise - Modular, agent-based architecture that can rapidly adapt to market changes - Real-world validation through live capital deployment (training on actual trading) ## Strategic Focus - Scale the Large Investment Model to cover more asset classes and geographies - Continue hiring top-tier AI and engineering talent (founding roles for ML engineers, quant developers, and engineering lead) - Deliver consistent, systematic outperformance for clients through AI-native investment strategies ## Why Work Here - **Culture**: Fast-moving startup environment with mentorship and high ownership; “build alongside investors and technologists reinventing the future of finance and AI” - **Work model**: Hybrid (employees work both remotely and on-site; office in Palo Alto, CA) - **Perks**: High-quality dental, vision, and health care; relocation support available - **Visa sponsorship**: No H-1B sponsorship history found - **Engineering focus**: Good learning environment for building production-scale AI systems; roles include founding-level impact ## Sources 1. [poesis.ai](https://poesis.ai/) 2. [LinkedIn – Poesis AI](https://www.linkedin.com/company/poesisai) 3. [Built In – Poesis Inc.](https://builtin.com/company/poesis-inc) 4. [GetLatka – Poesis AI](https://getlatka.com/companies/poesis.ai) 5. [Scoutify – Poesis Careers](https://scoutify.com/companies/poesis/) ## Other roles at Poesis - [Head of AI](https://feeny.ai/job/head-of-ai-poesis-zurich-n5g2ah6wmsg4) — Zurich, Switzerland - [Member of Technical Staff | Data Engineering, Finance](https://feeny.ai/job/member-of-technical-staff-data-engineering-finance-poesis-menlo-park-76y28rbhdhap) — Menlo Park, CA - [Member of Technical Staff | Machine Learning](https://feeny.ai/job/member-of-technical-staff-machine-learning-poesis-menlo-park-q01hcr8xgkpw) — Menlo Park, CA - [Member of Technical Staff | Agentic AI](https://feeny.ai/job/member-of-technical-staff-agentic-ai-poesis-menlo-park-456wbxpqnsa5) — Menlo Park, CA