Senior Machine Learning Engineer II

Carta
London, England, United KingdomPosted 20 March 2026

Job Description

The Company You’ll Join Carta connects founders, investors, and limited partners through world-class software, purpose-built for everyone in venture capital, private equity and private credit. Trusted by 65,000+ companies in 160+ countries, Carta’s platform of software and services lays the groundwork so you can build, invest, and scale with confidence. Carta’s Fund Administration platform supports 9,000+ funds and SPVs, representing nearly $185B in assets under management, with tools designed to enhance the strategic impact of fund CFOs. Recognized by Fortune, Forbes, Fast Company, Inc. and Great Places to Work, Carta is shaping the future of private market infrastructure. Together, Carta is creating the end-to-end ERP platform for private markets. Traditional ERP solutions don’t work for Private Funds. Private capital markets need a comprehensive software solution to replace outdated spreadsheets and fragmented service providers. Carta’s software for the Office of the Fund CFO does just that - it’s a new category of software to make private markets look more like public markets - a connected ERP for private capital. For more information about our offices and culture, check out our Carta careers page . The Problems You'll Solve At Carta, our employees set out on a mission to unlock the power of equity ownership for more people in more places. We believe that the problems we solve today unlock the opportunities of tomorrow. As a Senior Machine Learning Engineer II at Carta, you'll spend your time building agentic systems to power new products, automate complex document workflows, and accelerate Carta's business. You will also work closely with the SRE team to develop Carta's infrastructure around LLM integration and agentic processes. Example responsibilities will include: Develop agentic systems to classify, extract and review data from complex financial and legal documents. Develop agentic systems for document acquisition and management. Partner with product managers, engineers, designers, and business teams to incorporate agentic and LLM-powered products and user-flows. Own, coordinate, and solve complex, cross-functional problems that extend beyond the traditional boundaries of machine learning. The Team You'll Work With At Carta, our Machine Learning team is on a mission to power data products and automation through cutting-edge techniques. We work closely with Carta's core business units and product teams to develop LLM-powered and agentic systems for document intelligence, fund administration, legal tech, customer onboarding and more. This role is foundational to our team, where you'll work with one of the world's most valuable datasets and document repositories. Our systems directly enable revenue growth and operational efficiency for the company. Our team consists of specialized Machine Learning scientists and engineers, collaborating with product analytics and data engineering experts. We foster a culture of partnership, working closely with teams across Carta to solve impactful problems. We strongly believe that being helpful accelerates results, and we support one another to achieve success at Carta. The Impact You'll Have You will own significant projects that directly align with the company's strategic initiatives in agentic systems, AI adoption and document intelligence. Your contributions will empower leaders across the organization to deliver innovative products, optimize operational efficiency, and make well-informed product decisions. Additionally, you will have the opportunity to develop foundational infrastructure for our team and establish best practices for integrating our agentic and LLM-powered systems into production environments. Your work will not only impact Carta's current machine learning capabilities but also pave the way for future engineers, enabling them to build upon your efforts seamlessly. About You Candidates must have a strong foundation in machine learning and softwar ... (truncated, view full listing at source)
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