--- title: 'Member of Technical Staff | Data Engineering, Finance at Poesis' canonical: 'https://feeny.ai/job/member-of-technical-staff-data-engineering-finance-poesis-menlo-park-76y28rbhdhap' type: 'job' last_seen: '2026-11-04' --- # Member of Technical Staff | Data Engineering, Finance at Poesis - **Company:** Poesis - **Location:** Menlo Park, CA - **Employment:** full-time - **Work type:** hybrid - **Posted:** 2026-10-29 - **Last confirmed live:** 2026-11-04 - **Apply:** https://jobs.ashbyhq.com/poesis/75d3e370-e29e-4118-a193-9a1812f07d06 ## 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 At Poesis, we're building an AI-native investment firm where data quality directly shapes what our models learn and the decisions they make. Financial data is complex, and making it accurate, consistent and reliable at scale is one of our most important engineering challenges. As a Member of Technical Staff focused on data engineering, you'll build reliable autonomous AI agents that detect and resolve complex data issues and strengthen the foundation of our investment process. This is an opportunity to pioneer a new approach to financial data engineering, with AI at the core of how data systems are built and operated. Challenges You'll Work On - Build reliable autonomous agents that detect, investigate and resolve financial data errors, knowing when to escalate to humans. - Tackle point-in-time accuracy, survivorship bias and other challenges that can undermine financial models. - Investigate and resolve complex data integrity issues, from conflicting prices and corporate actions to inconsistent security identifiers. - Build scalable pipelines to integrate new financial datasets and ensure consistency across sources. ## About You - You have deep, hands-on experience building production data pipelines and working with financial data at a quantitative investment firm, data provider or similar environment. - You have experience with Bloomberg, S&P Capital IQ, FactSet, LSEG/Refinitiv, MSCI Barra, or similar data providers. - You have tackled complex financial data issues involving corporate actions, security mapping and point-in-time accuracy, and understand their impact on backtesting and risk models. - You can independently troubleshoot complex data issues, work with vendors when needed and develop practical solutions. - You've built AI agents or are excited to develop autonomous systems that solve complex, real-world problems. ## Nice to Have - Experience with Snowflake, Databricks, Airflow, AWS, or related technologies. 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 | Quantitative Finance](https://feeny.ai/job/member-of-technical-staff-quantitative-finance-poesis-menlo-park-1w1nvw9w85nb) — 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